{"type":"survey","study_desc":{"title_statement":{"idno":"SYR_2021_RTFP_v02_M","title":"Monthly food price estimates by product and market","sub_title":"Syrian Arab Republic, 97 markets, 2011\/01\/01-2025\/11\/01, version 2025-11-24","alternate_title":"Real Time Food Prices"},"authoring_entity":[{"name":"Bo Pieter Johannes Andr\u00e9e","affiliation":"World Bank, Development Data Group (DECDG), Office of the Chief Statistician (DECCS)"}],"production_statement":{"funding_agencies":[{"name":"Foreign, Commonwealth & Development Office of the United Kingdom","abbreviation":"FCDO (formerly DFID)","grant":"","role":"Support to data analytics"},{"name":"Foreign, Commonwealth & Development Office of the United Kingdom","abbreviation":"FCDO (formerly DFID)","grant":"KP-P174529-KMCE-TF0B4149","role":"Data documentation and dissemination (FCV Data Platform)"},{"name":"Department of Foreign Affairs and Trade of Australia","abbreviation":"DFAT","grant":"TF0B6892","role":"Support to methodological development in low data regions"},{"name":"Department of Foreign Affairs and Trade of Australia","abbreviation":"DFAT","grant":"TF0B6579","role":"Support to methodological development in low data regions"},{"name":"Federal Ministry for Economic Cooperation and Development of Germany as part of the World Bank\u2019s Food Systems 2030 Multi-Donor Trust Fund","abbreviation":"BMZ","grant":"TF073570","role":"Expansion of coverage and maintenance"},{"name":"Federal Ministry for Economic Cooperation and Development of Germany as part of the World Bank\u2019s Food Systems 2030 Multi-Donor Trust Fund","abbreviation":"BMZ","grant":"TF0C0728","role":"Expansion of coverage and maintenance"}]},"series_statement":{"series_name":"Monthly food price estimates in fragile countries","series_info":"Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.\n      \nRTP consists of three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.\n - RTFP: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2021_RTFP_v02_M \n - RTEP: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2023_RTEP_v01_M \n - RTFX: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2023_RTFX_v01_M \n \nTo produce smooth price series, outliers in the data are often adjusted using non-parametric density estimation and other techniques. Generalized Auto-Regressive Conditional Heteroskedasticity models are used to estimate intra-month price ranges. These models allow for excess kurtosis using a Generalized Error Distribution (GED). Open, High, Low, and Close price estimates are provided based on the modeled time-varying price distributions.\n      \nData are produced from 2007 to the present and estimates are given for individual commodity items at geo-referenced market locations. Predicted data for missing entries are based on exchange rates, and price data available either at other market locations or from related price items.\n      \nRTP estimates of historical and current prices may serve as proxies for sub-national price inflation series or substitute national-level Consumer Price Inflation (CPI) indicators when complete information is unavailable. Therefore, RTP data may differ from other sources with official data, including the World Bank\u2019s International Comparison Program (ICP) or inflation series reported in the World Development Indicators.\n      \nThe following datasets are part of this sub-series: \n\n Country-level inflation: \n - All countries: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2021_RTFP-CTRY_v02_M \n\n Market-level estimates: \n - All countries: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2021_RTFP_v02_M \n - Afghanistan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/AFG_2021_RTFP_v02_M \n - Armenia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/ARM_2021_RTFP_v02_M \n - Bangladesh: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BGD_2021_RTFP_v02_M \n - Burkina Faso: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BFA_2021_RTFP_v02_M \n - Burundi: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BDI_2021_RTFP_v02_M \n - Cameroon: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/CMR_2021_RTFP_v02_M \n - Central African Republic: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/CAF_2021_RTFP_v02_M \n - Chad: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/TCD_2021_RTFP_v02_M \n - Congo, Dem. Rep.: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/COD_2021_RTFP_v02_M \n - Congo, Rep.: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/COG_2021_RTFP_v02_M \n - Gambia, The: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GMB_2021_RTFP_v02_M \n - Guinea: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GIN_2021_RTFP_v02_M \n - Guinea-Bissau: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GNB_2021_RTFP_v02_M \n - Haiti: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/HTI_2021_RTFP_v02_M \n - Indonesia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/IDN_2021_RTFP_v02_M \n - Iraq: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/IRQ_2021_RTFP_v02_M \n - Kenya: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/KEN_2021_RTFP_v02_M \n - Lao PDR: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LAO_2021_RTFP_v02_M \n - Lebanon: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBN_2021_RTFP_v02_M \n - Liberia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBR_2021_RTFP_v02_M \n - Libya: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBY_2021_RTFP_v02_M \n - Malawi: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MWI_2021_RTFP_v02_M \n - Mali: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MLI_2021_RTFP_v02_M \n - Mauritania: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MRT_2021_RTFP_v02_M \n - Mozambique: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MOZ_2021_RTFP_v02_M \n - Myanmar: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MMR_2021_RTFP_v02_M \n - Niger: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/NER_2021_RTFP_v02_M \n - Nigeria: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/NGA_2021_RTFP_v02_M \n - Philippines: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/PHL_2021_RTFP_v02_M \n - Senegal: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SEN_2021_RTFP_v02_M \n - Somalia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SOM_2021_RTFP_v02_M \n - South Sudan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SSD_2021_RTFP_v02_M \n - Sri Lanka: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LKA_2021_RTFP_v02_M \n - Sudan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SDN_2021_RTFP_v02_M \n - Syrian Arab Republic: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SYR_2021_RTFP_v02_M \n - Yemen, Rep.: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/YEM_2021_RTFP_v02_M \n"},"oth_id":[{"name":"World Food Programme (WFP)","role":"Source of market price data","affiliation":"United Nations"},{"name":"Food and Agriculture Organization (FAO)","role":"Source of market price data","affiliation":"United Nations"}],"version_statement":{"version_date":"2025\/11\/24","version_resp":"FCV Data Platform"},"study_info":{"keywords":[{"keyword":"Food Price Monitor"},{"keyword":"FPM"},{"keyword":"Real Time Food Prices"},{"keyword":"RTFP"},{"keyword":"Inflation"},{"keyword":"Food Price Inflation"},{"keyword":"Food security"},{"keyword":"Food Insecurity"},{"keyword":"Food Crisis"},{"keyword":"Famine"},{"keyword":"Fragility"},{"keyword":"FCS"},{"keyword":"FCV"},{"keyword":"Food Price Crisis"},{"keyword":"Commodity prices"},{"keyword":"Maize"},{"keyword":"Sorghum"},{"keyword":"Wheat"},{"keyword":"Rice"},{"keyword":"Agricultural prices"},{"keyword":"Food prices"},{"keyword":"Real Time Food Prices"},{"keyword":"Price measurement"},{"keyword":"Real-time exchange rate data"},{"keyword":"Commodity items"},{"keyword":"Geo-referenced market locations"},{"keyword":"Sub-national price inflation series"},{"keyword":"Consumer Price Inflation (CPI)"},{"keyword":"National-level CPI indicators"},{"keyword":"International Comparison Program (ICP)"},{"keyword":"Inflation series"},{"keyword":"Market prices"},{"keyword":"Price analysis"},{"keyword":"Real-time data"},{"keyword":"National prices"},{"keyword":"International prices"},{"keyword":"Price fluctuations"},{"keyword":"Price trends"},{"keyword":"Price Volatility"},{"keyword":"Syrian Arab Republic"}],"abstract":"Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. \n                    This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.\n                    \n                    A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.","time_periods":[{"start":"2011\/01\/01","end":"2025\/11\/01"}],"coll_dates":[{"start":"2011\/01\/01","end":"2025\/11\/01"}],"nation":[{"name":"Syrian Arab Republic","abbreviation":"SYR"}],"geog_coverage_notes":"The data cover the following sub-national areas: Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Market Average","geog_unit":"Sub-national level, Admin 2 (selected)","notes":"List of food products included in estimates (not all products are included in country-level estimates): apples, bananas, beans, bread, bulgur, carrots, cheese, chickpeas, dates, eggplants, eggs, fish tuna canned, lentils, livestock sheep two year old male, meat beef minced, meat chicken plucked, oil, parsley, potatoes, rice, salt, sugar, tomatoes, wheat flour, yogurt"},"method":{"data_collection":{"sources":[{"name":"World Food Programme (WFP)","origin":"https:\/\/data.humdata.org\/organization\/wfp?vocab_Topics=prices","characteristics":"World Food Programme (WFP); data published in the Humanitarian Data Exchange (HDX) data catalog at https:\/\/data.humdata.org\/"},{"name":"Food and Agriculture Organization (FAO)","origin":"https:\/\/fpma.fao.org\/giews\/fpmat4\/#\/dashboard\/tool\/domestic","characteristics":"Local exchange rates and implied exchange rates data derived from commodity pairs published by FAO through the Food Price Monitoring and Analysis (FMPA) Tool"}]}},"data_access":{"dataset_availability":{"access_place":"World Bank Microdata Library, FCV Collection","access_place_url":"https:\/\/microdata.worldbank.org"},"dataset_use":{"restrictions":"The estimates presented in this dataset are all based on publicly-available data.\n          The dataset of price estimates is published as open data.","disclaimer":"The RTFP data and metadata provided are \"as is\" and \"as available,\" and every effort is made to ensure their timeliness, accuracy, and completeness. When errors are discovered, they are corrected as appropriate and feasible. For details on the terms and conditions for usage of the RTFP database, please refer to the license details.","contact":[{"name":"Data Help Desk","affiliation":"World Bank, Development Data Group","uri":"https:\/\/datahelpdesk.worldbank.org\/"}],"cit_req":"Please cite this dataset as follows: Andr\u00e9e, B. P. J. (2021). Monthly food price estimates by product and market (Version 2025-11-24). SYR_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. https:\/\/doi.org\/10.48529\/2ZH0-JF55"}}},"tags":[{"tag":"DOI"},{"tag":"fpm"}],"additional":{"ticker_description":"Information on the model (see working paper for more information)","ticker_info":[{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"apples","full_name":"Apples","units":"KG","food_index_weight":"1","data_coverage":"29.54%","r2_imputation":"0.89"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"bananas","full_name":"Bananas","units":"KG","food_index_weight":"1","data_coverage":"30.11%","r2_imputation":"0.88"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"beans","full_name":"Beans (white)","units":"KG","food_index_weight":"1","data_coverage":"34.06%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"bread","full_name":"Bread (bakery)","units":"1.1 KG","food_index_weight":"0.9091","data_coverage":"37.45%","r2_imputation":"0.97"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"bulgur","full_name":"Bulgur","units":"KG","food_index_weight":"1","data_coverage":"33.76%","r2_imputation":"0.94"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"carrots","full_name":"Carrots","units":"KG","food_index_weight":"1","data_coverage":"27.55%","r2_imputation":"0.87"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"cheese","full_name":"Cheese","units":"KG","food_index_weight":"1","data_coverage":"32.57%","r2_imputation":"0.95"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"chickpeas","full_name":"Chickpeas","units":"KG","food_index_weight":"1","data_coverage":"31.79%","r2_imputation":"0.93"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"dates","full_name":"Dates","units":"KG","food_index_weight":"1","data_coverage":"34.48%","r2_imputation":"0.93"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"eggplants","full_name":"Eggplants","units":"KG","food_index_weight":"1","data_coverage":"29.69%","r2_imputation":"0.82"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"eggs","full_name":"Eggs","units":"30 pcs","food_index_weight":"0.3333","data_coverage":"30.47%","r2_imputation":"0.95"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"exchange_rate_unofficial","full_name":"Exchange rate (unofficial)","units":"USD\/LCU","food_index_weight":"0","data_coverage":"30.45%","r2_imputation":"0.94"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"fish_tuna_canned","full_name":"Fish (tuna, canned)","units":"160 G","food_index_weight":"6.25","data_coverage":"34.93%","r2_imputation":"0.95"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"food_price_index","full_name":"Food Price Index","units":"LCU, Indexed, January 2018 = 1","food_index_weight":"Laspreyes","data_coverage":"34.14%","r2_imputation":"0.94"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"fuel_diesel","full_name":"Fuel (diesel)","units":"L","food_index_weight":"0","data_coverage":"36.76%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"fuel_gas","full_name":"Fuel (gas)","units":"25 KG","food_index_weight":"0","data_coverage":"35.85%","r2_imputation":"0.95"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"lentils","full_name":"Lentils","units":"KG","food_index_weight":"1","data_coverage":"40.06%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"livestock_sheep_two_year_old_male","full_name":"Livestock (sheep, two-year-old male)","units":"Head","food_index_weight":"0","data_coverage":"35.72%","r2_imputation":"0.98"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"meat_beef_minced","full_name":"Meat (beef, minced)","units":"KG","food_index_weight":"1","data_coverage":"30.43%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"meat_chicken_plucked","full_name":"Meat (chicken, plucked)","units":"KG","food_index_weight":"1","data_coverage":"30.79%","r2_imputation":"0.94"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"oil","full_name":"Oil","units":"L","food_index_weight":"1","data_coverage":"40.86%","r2_imputation":"0.97"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"parsley","full_name":"Parsley","units":"Packet","food_index_weight":"2","data_coverage":"33.85%","r2_imputation":"0.94"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"potatoes","full_name":"Potatoes","units":"KG","food_index_weight":"1","data_coverage":"30.6%","r2_imputation":"0.91"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"rice","full_name":"Rice","units":"KG","food_index_weight":"1","data_coverage":"38.27%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"salt","full_name":"Salt (iodised)","units":"KG","food_index_weight":"1","data_coverage":"33.01%","r2_imputation":"0.93"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"sugar","full_name":"Sugar","units":"KG","food_index_weight":"1","data_coverage":"39.47%","r2_imputation":"0.97"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"tomatoes","full_name":"Tomatoes","units":"KG","food_index_weight":"1","data_coverage":"34.21%","r2_imputation":"0.91"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"wage_non_qualified_labour","full_name":"Wage (non-qualified labour)","units":"Day","food_index_weight":"0","data_coverage":"35.98%","r2_imputation":"0.95"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"wheat_flour","full_name":"Wheat flour","units":"KG","food_index_weight":"1","data_coverage":"38.41%","r2_imputation":"0.96"},{"iso3":"SYR","country":"Syrian Arab Republic","ticker":"yogurt","full_name":"Yogurt","units":"KG","food_index_weight":"1","data_coverage":"34.58%","r2_imputation":"0.94"}]},"schematype":"survey","data_files":[{"file_id":"SYR_2021_RTFP_MKT","file_name":"SYR_RTFP_mkt_2011_2025-11-24.csv","description":"Monthly price estimates at market\/commodity level (all available countries)","case_count":17542,"var_count":200,"producer":null,"data_checks":null,"version":"2025\/11\/24","notes":null,"metadata":null}],"variables":[{"file_id":"SYR_2021_RTFP_MKT","vid":"V001","name":"ISO3","labl":"Country code","var_txt":"ISO3C codes, also known as ISO 3166-1 alpha-3 codes, are three-letter country or territory codes that are part of the ISO 3166 international standard. These codes are used to uniquely represent and identify countries and dependent territories in a standardized manner. Each ISO3C code corresponds to a specific country or territory and is often used in various applications, such as international trade, banking, internet domain names, and statistical analysis, to simplify and standardize country and territory references.","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V002","name":"country","labl":"Country","var_txt":"","var_notes":"Country names follow their appearance in the World Bank World Development Indicators.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V003","name":"adm1_name","labl":"Area name (admin. level 1)","var_txt":"","var_notes":"Administrative names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V004","name":"adm2_name","labl":"Area name (admin. level 2)","var_txt":"","var_notes":"Administrative names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V005","name":"mkt_name","labl":"Market name","var_txt":"","var_notes":"The mkt_name variable represents the name of the market associated with each price data point in the dataset. This field provides a clear, textual identifier for each market location, offering a more intuitive and user-friendly way to reference and distinguish between different markets. The market name is crucial for qualitative analysis and for users familiar with regional market names, facilitating easy identification and comparison of market-specific trends and patterns. Market names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names. When analyzing market price data, geo_id helps in correlating price information with specific, named market locations, enhancing the contextual understanding of the data. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates, while the geo_id is unique for each location.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V006","name":"lat","labl":"Latitude","var_txt":"","var_notes":"Geographic positioning of the market location at which price data is tracked, expressed as a geographic coordinate that measure the east-west positioning on Earth relative to the Prime Meridian in Greenwich, England. Use the geo_id field to obtain a better understanding of unique market locations. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates.","var_sumstat":[{"type":"Number of valid values","value":17363}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V007","name":"lon","labl":"Longitude","var_txt":"","var_notes":"Geographic positioning of the market location at which price data is tracked, expressed as a geographic coordinate that measure the north-south positioning on Earth relative to the equator. Use the geo_id field to obtain a better understanding of unique market locations. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates.","var_sumstat":[{"type":"Number of valid values","value":17363}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V008","name":"geo_id","labl":"Market location identifier","var_txt":"","var_notes":"The geo_id variable serves as a unique identifier for each market location in the RTP datasets, derived from geographic coordinates. This identifier is essential for accurately linking market price data to specific geographical locations. It ensures precise tracking and comparison of prices across different areas and is particularly useful for spatial analysis and mapping trends geographically. The uniqueness of each geo_id aids in the clear distinction and aggregation of data by location, making it a key element in any geographical or location-based analysis of market prices. The geo_id is shared across Real Time Food Prices (RTFP), Real Time Energy Prices (RTEP) and Real Time Exchange Rates (RTFP) that share the same timestamp (RTP data are generated weekly, they share the same timestamp when they are in the same week). The geo_id may be used to link data sets from different time periods, but caution is recommended. Use also the mkt_name field and Longitude and Latitude to obtain a better understanding of the market location, while noting that market names may change over time, market names may be shared between multiple locations, or multiple markets may share similar coordinates.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V009","name":"year","labl":"Year","var_txt":"","var_notes":"The year variable represents the year associated with each market price data point, provided in numerical format (e.g., 2023). This field is allows segmenting and analyzing the price data on an annual basis.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V010","name":"month","labl":"Month","var_txt":"","var_notes":"The month variable indicates the month number (1-12) corresponding to each market price data point, presented in a numerical format. This field facilitates more granular temporal insights and may be used to calculate seasonal adjustments to inflation estimates by the user.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V011","name":"currency","labl":"Currency","var_txt":"","var_notes":"The currency variable specifies the currency unit in which each market price data point is denominated. This field is essential for ensuring accurate financial interpretation and comparison of market prices across different regions or countries. Note that all the data is in Local Currency Unit (LCU). In countries with multiple competing currencies, the currency with the highest response rate in the underlying price survey data is used. Note that for both Real Time Food Prices (RTFP) and Real Time Energy Prices (RTEP) the USD\/LCU variable in Real Time Exchange Rates (RTFP) can be used to dollarize the country data using area-specific estimates of prevailing unofficial retail exchange rates.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V012","name":"components","labl":"Components","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V013","name":"start_dense_data","labl":"Start dense data","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V014","name":"last_survey_point","labl":"Last survey point","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V015","name":"data_coverage","labl":"Data coverage","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V016","name":"data_coverage_recent","labl":"Data coverage recent","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V017","name":"index_confidence_score","labl":"Index confidence score","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V018","name":"spatially_interpolated","labl":"Spatial interpolation (0\/1)","var_txt":"","var_notes":"The spatially_interpolated variable is a binary indicator distinguishes between prices that are directly measured or estimated for a specific location at which prices are measured in other time periods, and those derived solely through spatial interpolation. When spatially_interpolated is set to true (or 1), it indicates that the price has been calculated using an inverse distance weighted interpolation method, based on data and estimates from nearby market locations. This is crucial for understanding the precision and context of each price point. The data is likely more accurate for the specific locations where data is or has been measured, while spatially interpolated prices provide estimations based only on surrounding data, useful for areas without direct measurements. This variable is key for assessing the reliability and geographical relevance of the price data.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V019","name":"apples","labl":"Apples","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4142}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V020","name":"bananas","labl":"Bananas","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4461}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V021","name":"beans","labl":"Beans","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4984}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V022","name":"bread","labl":"Bread","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5568}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V023","name":"bulgur","labl":"Bulgur","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5078}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V024","name":"carrots","labl":"Carrots","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4164}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V025","name":"cheese","labl":"Cheese","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4551}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V026","name":"chickpeas","labl":"Chickpeas","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4613}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V027","name":"dates","labl":"Dates","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5066}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V028","name":"eggplants","labl":"Eggplants","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":3990}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V029","name":"eggs","labl":"Eggs","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4291}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V030","name":"fish_tuna_canned","labl":"Fish tuna canned","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5020}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V031","name":"lentils","labl":"Lentils","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":6098}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V032","name":"livestock_sheep_two_year_old_male","labl":"Livestock sheep two year old male","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5442}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V033","name":"meat_beef_minced","labl":"Meat beef minced","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4579}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V034","name":"meat_chicken_plucked","labl":"Meat chicken plucked","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4756}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V035","name":"oil","labl":"Oil","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":6054}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V036","name":"parsley","labl":"Parsley","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4591}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V037","name":"potatoes","labl":"Potatoes","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4380}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V038","name":"rice","labl":"Rice","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5394}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V039","name":"salt","labl":"Salt","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5047}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V040","name":"sugar","labl":"Sugar","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5454}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V041","name":"tomatoes","labl":"Tomatoes","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5031}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V042","name":"wheat_flour","labl":"Wheat flour","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":5354}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V043","name":"yogurt","labl":"Yogurt","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":4783}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V044","name":"o_apples","labl":"Open estimate - Apples","var_txt":"","var_notes":"o_apples indicates the monthly opening price estimate for the commodity apples. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V045","name":"h_apples","labl":"High estimate - Apples","var_txt":"","var_notes":"h_apples denotes the highest price achieved by the commodity apples within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V046","name":"l_apples","labl":"Low estimate - Apples","var_txt":"","var_notes":"l_apples represents the lowest price point for the commodity apples in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V047","name":"c_apples","labl":"Close estimate - Apples","var_txt":"","var_notes":"c_apples is the closing price estimate for the commodity apples at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V048","name":"inflation_apples","labl":"Inflation - Apples","var_txt":"","var_notes":"inflation_apples provides the 12-month inflation rate, or price change rate, for commodity apples. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V049","name":"trust_apples","labl":"Trust - Apples","var_txt":"","var_notes":"trust_apples offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for apples. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V050","name":"o_bananas","labl":"Open estimate - Bananas","var_txt":"","var_notes":"o_bananas indicates the monthly opening price estimate for the commodity bananas. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V051","name":"h_bananas","labl":"High estimate - Bananas","var_txt":"","var_notes":"h_bananas denotes the highest price achieved by the commodity bananas within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V052","name":"l_bananas","labl":"Low estimate - Bananas","var_txt":"","var_notes":"l_bananas represents the lowest price point for the commodity bananas in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V053","name":"c_bananas","labl":"Close estimate - Bananas","var_txt":"","var_notes":"c_bananas is the closing price estimate for the commodity bananas at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V054","name":"inflation_bananas","labl":"Inflation - Bananas","var_txt":"","var_notes":"inflation_bananas provides the 12-month inflation rate, or price change rate, for commodity bananas. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V055","name":"trust_bananas","labl":"Trust - Bananas","var_txt":"","var_notes":"trust_bananas offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for bananas. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V056","name":"o_beans","labl":"Open estimate - Beans","var_txt":"","var_notes":"o_beans indicates the monthly opening price estimate for the commodity beans. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V057","name":"h_beans","labl":"High estimate - Beans","var_txt":"","var_notes":"h_beans denotes the highest price achieved by the commodity beans within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V058","name":"l_beans","labl":"Low estimate - Beans","var_txt":"","var_notes":"l_beans represents the lowest price point for the commodity beans in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V059","name":"c_beans","labl":"Close estimate - Beans","var_txt":"","var_notes":"c_beans is the closing price estimate for the commodity beans at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V060","name":"inflation_beans","labl":"Inflation - Beans","var_txt":"","var_notes":"inflation_beans provides the 12-month inflation rate, or price change rate, for commodity beans. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V061","name":"trust_beans","labl":"Trust - Beans","var_txt":"","var_notes":"trust_beans offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for beans. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V062","name":"o_bread","labl":"Open estimate - Bread","var_txt":"","var_notes":"o_bread indicates the monthly opening price estimate for the commodity bread. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V063","name":"h_bread","labl":"High estimate - Bread","var_txt":"","var_notes":"h_bread denotes the highest price achieved by the commodity bread within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V064","name":"l_bread","labl":"Low estimate - Bread","var_txt":"","var_notes":"l_bread represents the lowest price point for the commodity bread in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V065","name":"c_bread","labl":"Close estimate - Bread","var_txt":"","var_notes":"c_bread is the closing price estimate for the commodity bread at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V066","name":"inflation_bread","labl":"Inflation - Bread","var_txt":"","var_notes":"inflation_bread provides the 12-month inflation rate, or price change rate, for commodity bread. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V067","name":"trust_bread","labl":"Trust - Bread","var_txt":"","var_notes":"trust_bread offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for bread. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V068","name":"o_bulgur","labl":"Open estimate - Bulgur","var_txt":"","var_notes":"o_bulgur indicates the monthly opening price estimate for the commodity bulgur. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V069","name":"h_bulgur","labl":"High estimate - Bulgur","var_txt":"","var_notes":"h_bulgur denotes the highest price achieved by the commodity bulgur within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V070","name":"l_bulgur","labl":"Low estimate - Bulgur","var_txt":"","var_notes":"l_bulgur represents the lowest price point for the commodity bulgur in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V071","name":"c_bulgur","labl":"Close estimate - Bulgur","var_txt":"","var_notes":"c_bulgur is the closing price estimate for the commodity bulgur at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V072","name":"inflation_bulgur","labl":"Inflation - Bulgur","var_txt":"","var_notes":"inflation_bulgur provides the 12-month inflation rate, or price change rate, for commodity bulgur. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V073","name":"trust_bulgur","labl":"Trust - Bulgur","var_txt":"","var_notes":"trust_bulgur offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for bulgur. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V074","name":"o_carrots","labl":"Open estimate - Carrots","var_txt":"","var_notes":"o_carrots indicates the monthly opening price estimate for the commodity carrots. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V075","name":"h_carrots","labl":"High estimate - Carrots","var_txt":"","var_notes":"h_carrots denotes the highest price achieved by the commodity carrots within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V076","name":"l_carrots","labl":"Low estimate - Carrots","var_txt":"","var_notes":"l_carrots represents the lowest price point for the commodity carrots in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V077","name":"c_carrots","labl":"Close estimate - Carrots","var_txt":"","var_notes":"c_carrots is the closing price estimate for the commodity carrots at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V078","name":"inflation_carrots","labl":"Inflation - Carrots","var_txt":"","var_notes":"inflation_carrots provides the 12-month inflation rate, or price change rate, for commodity carrots. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V079","name":"trust_carrots","labl":"Trust - Carrots","var_txt":"","var_notes":"trust_carrots offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for carrots. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V080","name":"o_cheese","labl":"Open estimate - Cheese","var_txt":"","var_notes":"o_cheese indicates the monthly opening price estimate for the commodity cheese. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V081","name":"h_cheese","labl":"High estimate - Cheese","var_txt":"","var_notes":"h_cheese denotes the highest price achieved by the commodity cheese within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V082","name":"l_cheese","labl":"Low estimate - Cheese","var_txt":"","var_notes":"l_cheese represents the lowest price point for the commodity cheese in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V083","name":"c_cheese","labl":"Close estimate - Cheese","var_txt":"","var_notes":"c_cheese is the closing price estimate for the commodity cheese at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V084","name":"inflation_cheese","labl":"Inflation - Cheese","var_txt":"","var_notes":"inflation_cheese provides the 12-month inflation rate, or price change rate, for commodity cheese. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V085","name":"trust_cheese","labl":"Trust - Cheese","var_txt":"","var_notes":"trust_cheese offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for cheese. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V086","name":"o_chickpeas","labl":"Open estimate - Chickpeas","var_txt":"","var_notes":"o_chickpeas indicates the monthly opening price estimate for the commodity chickpeas. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V087","name":"h_chickpeas","labl":"High estimate - Chickpeas","var_txt":"","var_notes":"h_chickpeas denotes the highest price achieved by the commodity chickpeas within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V088","name":"l_chickpeas","labl":"Low estimate - Chickpeas","var_txt":"","var_notes":"l_chickpeas represents the lowest price point for the commodity chickpeas in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V089","name":"c_chickpeas","labl":"Close estimate - Chickpeas","var_txt":"","var_notes":"c_chickpeas is the closing price estimate for the commodity chickpeas at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V090","name":"inflation_chickpeas","labl":"Inflation - Chickpeas","var_txt":"","var_notes":"inflation_chickpeas provides the 12-month inflation rate, or price change rate, for commodity chickpeas. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V091","name":"trust_chickpeas","labl":"Trust - Chickpeas","var_txt":"","var_notes":"trust_chickpeas offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for chickpeas. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V092","name":"o_dates","labl":"Open estimate - Dates","var_txt":"","var_notes":"o_dates indicates the monthly opening price estimate for the commodity dates. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V093","name":"h_dates","labl":"High estimate - Dates","var_txt":"","var_notes":"h_dates denotes the highest price achieved by the commodity dates within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V094","name":"l_dates","labl":"Low estimate - Dates","var_txt":"","var_notes":"l_dates represents the lowest price point for the commodity dates in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V095","name":"c_dates","labl":"Close estimate - Dates","var_txt":"","var_notes":"c_dates is the closing price estimate for the commodity dates at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V096","name":"inflation_dates","labl":"Inflation - Dates","var_txt":"","var_notes":"inflation_dates provides the 12-month inflation rate, or price change rate, for commodity dates. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V097","name":"trust_dates","labl":"Trust - Dates","var_txt":"","var_notes":"trust_dates offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for dates. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V098","name":"o_eggplants","labl":"Open estimate - Eggplants","var_txt":"","var_notes":"o_eggplants indicates the monthly opening price estimate for the commodity eggplants. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V099","name":"h_eggplants","labl":"High estimate - Eggplants","var_txt":"","var_notes":"h_eggplants denotes the highest price achieved by the commodity eggplants within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V100","name":"l_eggplants","labl":"Low estimate - Eggplants","var_txt":"","var_notes":"l_eggplants represents the lowest price point for the commodity eggplants in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V101","name":"c_eggplants","labl":"Close estimate - Eggplants","var_txt":"","var_notes":"c_eggplants is the closing price estimate for the commodity eggplants at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V102","name":"inflation_eggplants","labl":"Inflation - Eggplants","var_txt":"","var_notes":"inflation_eggplants provides the 12-month inflation rate, or price change rate, for commodity eggplants. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V103","name":"trust_eggplants","labl":"Trust - Eggplants","var_txt":"","var_notes":"trust_eggplants offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for eggplants. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V104","name":"o_eggs","labl":"Open estimate - Eggs","var_txt":"","var_notes":"o_eggs indicates the monthly opening price estimate for the commodity eggs. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V105","name":"h_eggs","labl":"High estimate - Eggs","var_txt":"","var_notes":"h_eggs denotes the highest price achieved by the commodity eggs within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V106","name":"l_eggs","labl":"Low estimate - Eggs","var_txt":"","var_notes":"l_eggs represents the lowest price point for the commodity eggs in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V107","name":"c_eggs","labl":"Close estimate - Eggs","var_txt":"","var_notes":"c_eggs is the closing price estimate for the commodity eggs at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V108","name":"inflation_eggs","labl":"Inflation - Eggs","var_txt":"","var_notes":"inflation_eggs provides the 12-month inflation rate, or price change rate, for commodity eggs. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V109","name":"trust_eggs","labl":"Trust - Eggs","var_txt":"","var_notes":"trust_eggs offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for eggs. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V110","name":"o_fish_tuna_canned","labl":"Open estimate - Fish tuna canned","var_txt":"","var_notes":"o_fistuna_canned indicates the monthly opening price estimate for the commodity fistuna_canned. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V111","name":"h_fish_tuna_canned","labl":"High estimate - Fistuna canned","var_txt":"","var_notes":"h_fistuna_canned denotes the highest price achieved by the commodity fistuna_canned within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V112","name":"l_fish_tuna_canned","labl":"Low estimate - Fish tuna canned","var_txt":"","var_notes":"l_fistuna_canned represents the lowest price point for the commodity fistuna_canned in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V113","name":"c_fish_tuna_canned","labl":"Close estimate - Fish tuna canned","var_txt":"","var_notes":"c_fistuna_canned is the closing price estimate for the commodity fistuna_canned at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V114","name":"inflation_fish_tuna_canned","labl":"Inflation - Fish tuna canned","var_txt":"","var_notes":"inflation_fistuna_canned provides the 12-month inflation rate, or price change rate, for commodity fistuna_canned. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V115","name":"trust_fish_tuna_canned","labl":"Trust - Fish tuna canned","var_txt":"","var_notes":"trust_fistuna_canned offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for fistuna_canned. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V116","name":"o_lentils","labl":"Open estimate - Lentils","var_txt":"","var_notes":"o_lentils indicates the monthly opening price estimate for the commodity lentils. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V117","name":"h_lentils","labl":"High estimate - Lentils","var_txt":"","var_notes":"h_lentils denotes the highest price achieved by the commodity lentils within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V118","name":"l_lentils","labl":"Low estimate - Lentils","var_txt":"","var_notes":"l_lentils represents the lowest price point for the commodity lentils in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V119","name":"c_lentils","labl":"Close estimate - Lentils","var_txt":"","var_notes":"c_lentils is the closing price estimate for the commodity lentils at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V120","name":"inflation_lentils","labl":"Inflation - Lentils","var_txt":"","var_notes":"inflation_lentils provides the 12-month inflation rate, or price change rate, for commodity lentils. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V121","name":"trust_lentils","labl":"Trust - Lentils","var_txt":"","var_notes":"trust_lentils offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for lentils. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V122","name":"o_livestock_sheep_two_year_old_male","labl":"Open estimate - Livestock sheep twyear old male","var_txt":"","var_notes":"o_livestock_sheep_twyear_old_male indicates the monthly opening price estimate for the commodity livestock_sheep_twyear_old_male. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V123","name":"h_livestock_sheep_two_year_old_male","labl":"High estimate - Livestock sheep two year old male","var_txt":"","var_notes":"h_livestock_sheep_twyear_old_male denotes the highest price achieved by the commodity livestock_sheep_twyear_old_male within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V124","name":"l_livestock_sheep_two_year_old_male","labl":"Low estimate - Livestock sheep two year old male","var_txt":"","var_notes":"l_livestock_sheep_twyear_old_male represents the lowest price point for the commodity livestock_sheep_twyear_old_male in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V125","name":"c_livestock_sheep_two_year_old_male","labl":"Close estimate - Livestock sheep two year old male","var_txt":"","var_notes":"c_livestock_sheep_twyear_old_male is the closing price estimate for the commodity livestock_sheep_twyear_old_male at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V126","name":"inflation_livestock_sheep_two_year_old_male","labl":"Inflation - Livestock sheep two year old male","var_txt":"","var_notes":"inflation_livestock_sheep_twyear_old_male provides the 12-month inflation rate, or price change rate, for commodity livestock_sheep_twyear_old_male. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V127","name":"trust_livestock_sheep_two_year_old_male","labl":"Trust - Livestock sheep two year old male","var_txt":"","var_notes":"trust_livestock_sheep_twyear_old_male offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for livestock_sheep_twyear_old_male. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V128","name":"o_meat_beef_minced","labl":"Open estimate - Meat beef minced","var_txt":"","var_notes":"o_meat_beef_minced indicates the monthly opening price estimate for the commodity meat_beef_minced. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V129","name":"h_meat_beef_minced","labl":"High estimate - Meat beef minced","var_txt":"","var_notes":"h_meat_beef_minced denotes the highest price achieved by the commodity meat_beef_minced within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V130","name":"l_meat_beef_minced","labl":"Low estimate - Meat beef minced","var_txt":"","var_notes":"l_meat_beef_minced represents the lowest price point for the commodity meat_beef_minced in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V131","name":"c_meat_beef_minced","labl":"Close estimate - Meat beef minced","var_txt":"","var_notes":"c_meat_beef_minced is the closing price estimate for the commodity meat_beef_minced at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V132","name":"inflation_meat_beef_minced","labl":"Inflation - Meat beef minced","var_txt":"","var_notes":"inflation_meat_beef_minced provides the 12-month inflation rate, or price change rate, for commodity meat_beef_minced. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V133","name":"trust_meat_beef_minced","labl":"Trust - Meat beef minced","var_txt":"","var_notes":"trust_meat_beef_minced offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for meat_beef_minced. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V134","name":"o_meat_chicken_plucked","labl":"Open estimate - Meat chicken plucked","var_txt":"","var_notes":"o_meat_chicken_plucked indicates the monthly opening price estimate for the commodity meat_chicken_plucked. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V135","name":"h_meat_chicken_plucked","labl":"High estimate - Meat chicken plucked","var_txt":"","var_notes":"h_meat_chicken_plucked denotes the highest price achieved by the commodity meat_chicken_plucked within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V136","name":"l_meat_chicken_plucked","labl":"Low estimate - Meat chicken plucked","var_txt":"","var_notes":"l_meat_chicken_plucked represents the lowest price point for the commodity meat_chicken_plucked in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V137","name":"c_meat_chicken_plucked","labl":"Close estimate - Meat chicken plucked","var_txt":"","var_notes":"c_meat_chicken_plucked is the closing price estimate for the commodity meat_chicken_plucked at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V138","name":"inflation_meat_chicken_plucked","labl":"Inflation - Meat chicken plucked","var_txt":"","var_notes":"inflation_meat_chicken_plucked provides the 12-month inflation rate, or price change rate, for commodity meat_chicken_plucked. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V139","name":"trust_meat_chicken_plucked","labl":"Trust - Meat chicken plucked","var_txt":"","var_notes":"trust_meat_chicken_plucked offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for meat_chicken_plucked. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V140","name":"o_oil","labl":"Open estimate - Oil","var_txt":"","var_notes":"o_oil indicates the monthly opening price estimate for the commodity oil. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V141","name":"h_oil","labl":"High estimate - Oil","var_txt":"","var_notes":"h_oil denotes the highest price achieved by the commodity oil within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V142","name":"l_oil","labl":"Low estimate - Oil","var_txt":"","var_notes":"l_oil represents the lowest price point for the commodity oil in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V143","name":"c_oil","labl":"Close estimate - Oil","var_txt":"","var_notes":"c_oil is the closing price estimate for the commodity oil at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V144","name":"inflation_oil","labl":"Inflation - Oil","var_txt":"","var_notes":"inflation_oil provides the 12-month inflation rate, or price change rate, for commodity oil. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V145","name":"trust_oil","labl":"Trust - Oil","var_txt":"","var_notes":"trust_oil offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for oil. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V146","name":"o_parsley","labl":"Open estimate - Parsley","var_txt":"","var_notes":"o_parsley indicates the monthly opening price estimate for the commodity parsley. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V147","name":"h_parsley","labl":"High estimate - Parsley","var_txt":"","var_notes":"h_parsley denotes the highest price achieved by the commodity parsley within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V148","name":"l_parsley","labl":"Low estimate - Parsley","var_txt":"","var_notes":"l_parsley represents the lowest price point for the commodity parsley in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V149","name":"c_parsley","labl":"Close estimate - Parsley","var_txt":"","var_notes":"c_parsley is the closing price estimate for the commodity parsley at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V150","name":"inflation_parsley","labl":"Inflation - Parsley","var_txt":"","var_notes":"inflation_parsley provides the 12-month inflation rate, or price change rate, for commodity parsley. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V151","name":"trust_parsley","labl":"Trust - Parsley","var_txt":"","var_notes":"trust_parsley offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for parsley. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V152","name":"o_potatoes","labl":"Open estimate - Potatoes","var_txt":"","var_notes":"o_potatoes indicates the monthly opening price estimate for the commodity potatoes. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V153","name":"h_potatoes","labl":"High estimate - Potatoes","var_txt":"","var_notes":"h_potatoes denotes the highest price achieved by the commodity potatoes within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V154","name":"l_potatoes","labl":"Low estimate - Potatoes","var_txt":"","var_notes":"l_potatoes represents the lowest price point for the commodity potatoes in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V155","name":"c_potatoes","labl":"Close estimate - Potatoes","var_txt":"","var_notes":"c_potatoes is the closing price estimate for the commodity potatoes at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V156","name":"inflation_potatoes","labl":"Inflation - Potatoes","var_txt":"","var_notes":"inflation_potatoes provides the 12-month inflation rate, or price change rate, for commodity potatoes. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V157","name":"trust_potatoes","labl":"Trust - Potatoes","var_txt":"","var_notes":"trust_potatoes offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for potatoes. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V158","name":"o_rice","labl":"Open estimate - Rice","var_txt":"","var_notes":"o_rice indicates the monthly opening price estimate for the commodity rice. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V159","name":"h_rice","labl":"High estimate - Rice","var_txt":"","var_notes":"h_rice denotes the highest price achieved by the commodity rice within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V160","name":"l_rice","labl":"Low estimate - Rice","var_txt":"","var_notes":"l_rice represents the lowest price point for the commodity rice in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V161","name":"c_rice","labl":"Close estimate - Rice","var_txt":"","var_notes":"c_rice is the closing price estimate for the commodity rice at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V162","name":"inflation_rice","labl":"Inflation - Rice","var_txt":"","var_notes":"inflation_rice provides the 12-month inflation rate, or price change rate, for commodity rice. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V163","name":"trust_rice","labl":"Trust - Rice","var_txt":"","var_notes":"trust_rice offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for rice. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V164","name":"o_salt","labl":"Open estimate - Salt","var_txt":"","var_notes":"o_salt indicates the monthly opening price estimate for the commodity salt. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V165","name":"h_salt","labl":"High estimate - Salt","var_txt":"","var_notes":"h_salt denotes the highest price achieved by the commodity salt within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V166","name":"l_salt","labl":"Low estimate - Salt","var_txt":"","var_notes":"l_salt represents the lowest price point for the commodity salt in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V167","name":"c_salt","labl":"Close estimate - Salt","var_txt":"","var_notes":"c_salt is the closing price estimate for the commodity salt at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V168","name":"inflation_salt","labl":"Inflation - Salt","var_txt":"","var_notes":"inflation_salt provides the 12-month inflation rate, or price change rate, for commodity salt. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V169","name":"trust_salt","labl":"Trust - Salt","var_txt":"","var_notes":"trust_salt offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for salt. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V170","name":"o_sugar","labl":"Open estimate - Sugar","var_txt":"","var_notes":"o_sugar indicates the monthly opening price estimate for the commodity sugar. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V171","name":"h_sugar","labl":"High estimate - Sugar","var_txt":"","var_notes":"h_sugar denotes the highest price achieved by the commodity sugar within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V172","name":"l_sugar","labl":"Low estimate - Sugar","var_txt":"","var_notes":"l_sugar represents the lowest price point for the commodity sugar in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V173","name":"c_sugar","labl":"Close estimate - Sugar","var_txt":"","var_notes":"c_sugar is the closing price estimate for the commodity sugar at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V174","name":"inflation_sugar","labl":"Inflation - Sugar","var_txt":"","var_notes":"inflation_sugar provides the 12-month inflation rate, or price change rate, for commodity sugar. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V175","name":"trust_sugar","labl":"Trust - Sugar","var_txt":"","var_notes":"trust_sugar offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for sugar. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V176","name":"o_tomatoes","labl":"Open estimate - Tomatoes","var_txt":"","var_notes":"o_tomatoes indicates the monthly opening price estimate for the commodity tomatoes. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V177","name":"h_tomatoes","labl":"High estimate - Tomatoes","var_txt":"","var_notes":"h_tomatoes denotes the highest price achieved by the commodity tomatoes within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V178","name":"l_tomatoes","labl":"Low estimate - Tomatoes","var_txt":"","var_notes":"l_tomatoes represents the lowest price point for the commodity tomatoes in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V179","name":"c_tomatoes","labl":"Close estimate - Tomatoes","var_txt":"","var_notes":"c_tomatoes is the closing price estimate for the commodity tomatoes at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V180","name":"inflation_tomatoes","labl":"Inflation - Tomatoes","var_txt":"","var_notes":"inflation_tomatoes provides the 12-month inflation rate, or price change rate, for commodity tomatoes. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V181","name":"trust_tomatoes","labl":"Trust - Tomatoes","var_txt":"","var_notes":"trust_tomatoes offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for tomatoes. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V182","name":"o_wheat_flour","labl":"Open estimate - Wheat flour","var_txt":"","var_notes":"o_wheat_flour indicates the monthly opening price estimate for the commodity wheat_flour. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V183","name":"h_wheat_flour","labl":"High estimate - Wheat flour","var_txt":"","var_notes":"h_wheat_flour denotes the highest price achieved by the commodity wheat_flour within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V184","name":"l_wheat_flour","labl":"Low estimate - Wheat flour","var_txt":"","var_notes":"l_wheat_flour represents the lowest price point for the commodity wheat_flour in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V185","name":"c_wheat_flour","labl":"Close estimate - Wheat flour","var_txt":"","var_notes":"c_wheat_flour is the closing price estimate for the commodity wheat_flour at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V186","name":"inflation_wheat_flour","labl":"Inflation - Wheat flour","var_txt":"","var_notes":"inflation_wheat_flour provides the 12-month inflation rate, or price change rate, for commodity wheat_flour. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V187","name":"trust_wheat_flour","labl":"Trust - Wheat flour","var_txt":"","var_notes":"trust_wheat_flour offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for wheat_flour. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V188","name":"o_yogurt","labl":"Open estimate - Yogurt","var_txt":"","var_notes":"o_yogurt indicates the monthly opening price estimate for the commodity yogurt. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V189","name":"h_yogurt","labl":"High estimate - Yogurt","var_txt":"","var_notes":"h_yogurt denotes the highest price achieved by the commodity yogurt within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V190","name":"l_yogurt","labl":"Low estimate - Yogurt","var_txt":"","var_notes":"l_yogurt represents the lowest price point for the commodity yogurt in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V191","name":"c_yogurt","labl":"Close estimate - Yogurt","var_txt":"","var_notes":"c_yogurt is the closing price estimate for the commodity yogurt at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V192","name":"inflation_yogurt","labl":"Inflation - Yogurt","var_txt":"","var_notes":"inflation_yogurt provides the 12-month inflation rate, or price change rate, for commodity yogurt. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V193","name":"trust_yogurt","labl":"Trust - Yogurt","var_txt":"","var_notes":"trust_yogurt offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for yogurt. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V194","name":"o_food_price_index","labl":"Open estimate - Food price index","var_txt":"","var_notes":"o_food_price_index indicates the monthly opening price estimate for the commodity food_price_index. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V195","name":"h_food_price_index","labl":"High estimate - Food price index","var_txt":"","var_notes":"h_food_price_index denotes the highest price achieved by the commodity food_price_index within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V196","name":"l_food_price_index","labl":"Low estimate - Food price index","var_txt":"","var_notes":"l_food_price_index represents the lowest price point for the commodity food_price_index in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V197","name":"c_food_price_index","labl":"Close estimate - Food price index","var_txt":"","var_notes":"c_food_price_index is the closing price estimate for the commodity food_price_index at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V198","name":"inflation_food_price_index","labl":"Inflation - Food price index","var_txt":"","var_notes":"inflation_food_price_index provides the 12-month inflation rate, or price change rate, for commodity food_price_index. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment.","var_sumstat":[{"type":"Number of valid values","value":16366}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V199","name":"trust_food_price_index","labl":"Trust - Food price index","var_txt":"","var_notes":"trust_food_price_index offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for food_price_index. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null},{"file_id":"SYR_2021_RTFP_MKT","vid":"V200","name":"DATES","labl":"Date in yyyy-mm-dd format","var_txt":"","var_notes":"For comparing historical data, forecasting, or daily analysis, price_date provides a temporal reference. The field corresponds to the dates of each market price data point, formatted as yyyy-mm-dd. It denotes when prices were recorded or the time period for which price data are predicted, enabling chronological analysis and trend tracking of completed market and commodity price series.","var_sumstat":[{"type":"Number of valid values","value":17542}],"fid":"SYR_2021_RTFP_MKT","qstn":"","catgry":null}],"variable_groups":[]}