{"type":"survey","study_desc":{"title_statement":{"idno":"AFG_2023_RTFX_v01_M","title":"Monthly currency exchange rate estimates by market","sub_title":"Afghanistan, 40 markets, 2007\/01\/01-2026\/08\/01, version 2026-08-24","alternate_title":"Real Time FX Rates"},"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 currency exchange rate 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 - All countries: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/WLD_2023_RTFX_v01_M \n - Afghanistan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/AFG_2023_RTFX_v01_M \n - Armenia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/ARM_2023_RTFX_v01_M \n - Bangladesh: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BGD_2023_RTFX_v01_M \n - Burkina Faso: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BFA_2023_RTFX_v01_M \n - Burundi: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/BDI_2023_RTFX_v01_M \n - Cameroon: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/CMR_2023_RTFX_v01_M \n - Central African Republic: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/CAF_2023_RTFX_v01_M \n - Chad: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/TCD_2023_RTFX_v01_M \n - Congo, Dem. Rep.: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/COD_2023_RTFX_v01_M \n - Congo, Rep.: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/COG_2023_RTFX_v01_M \n - Ethiopia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/ETH_2023_RTFX_v01_M \n - Gambia, The: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GMB_2023_RTFX_v01_M \n - Guatemala: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GTM_2023_RTFX_v01_M \n - Guinea: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GIN_2023_RTFX_v01_M \n - Guinea-Bissau: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/GNB_2023_RTFX_v01_M \n - Haiti: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/HTI_2023_RTFX_v01_M \n - Indonesia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/IDN_2023_RTFX_v01_M \n - Iraq: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/IRQ_2023_RTFX_v01_M \n - Kenya: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/KEN_2023_RTFX_v01_M \n - Lao PDR: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LAO_2023_RTFX_v01_M \n - Lebanon: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBN_2023_RTFX_v01_M \n - Liberia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBR_2023_RTFX_v01_M \n - Libya: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LBY_2023_RTFX_v01_M \n - Madagascar: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MDG_2023_RTFX_v01_M \n - Malawi: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MWI_2023_RTFX_v01_M \n - Mali: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MLI_2023_RTFX_v01_M \n - Mauritania: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MRT_2023_RTFX_v01_M \n - Mozambique: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MOZ_2023_RTFX_v01_M \n - Myanmar: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/MMR_2023_RTFX_v01_M \n - Niger: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/NER_2023_RTFX_v01_M \n - Nigeria: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/NGA_2023_RTFX_v01_M \n - Senegal: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SEN_2023_RTFX_v01_M \n - Somalia: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SOM_2023_RTFX_v01_M \n - South Sudan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SSD_2023_RTFX_v01_M \n - Sri Lanka: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/LKA_2023_RTFX_v01_M \n - Sudan: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/SDN_2023_RTFX_v01_M \n - Uganda: https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/UGA_2023_RTFX_v01_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":"2026\/08\/24","version_resp":"FCV Data Platform"},"study_info":{"keywords":[{"keyword":"Foreign Exchange Monitor"},{"keyword":"FXM"},{"keyword":"Real Time Foreign Exchange Rates"},{"keyword":"RTFX"},{"keyword":"Inflation"},{"keyword":"FCV"},{"keyword":"Real-time exchange rate data"},{"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":"Afghanistan"}],"abstract":"Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. \n                    This data set includes currency exchange rate 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.","time_periods":[{"start":"2007\/01\/01","end":"2026\/08\/01"}],"coll_dates":[{"start":"2007\/01\/01","end":"2026\/08\/01"}],"nation":[{"name":"Afghanistan","abbreviation":"AFG"}],"geog_coverage_notes":"The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average","geog_unit":"Sub-national level, Admin 2 (selected)","notes":"List of products included in estimates (not all products are included in country-level estimates): exchange rate unofficial, wage non qualified labour non agricultural, wage qualified labour"},"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 RTFX 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 RTFX 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":""}}},"tags":[{"tag":"fxm"},{"tag":"rtp"}],"additional":{"ticker_description":"Information on the model (see working paper for more information)","ticker_info":[{"iso3":"AFG","country":"Afghanistan","ticker":"bread","full_name":"Bread","units":"KG","food_index_weight":"1","data_coverage":"37.05%","r2_imputation":"0.95"},{"iso3":"AFG","country":"Afghanistan","ticker":"exchange_rate_unofficial","full_name":"Exchange rate","units":"USD\/LCU","food_index_weight":"0","data_coverage":"59.25%","r2_imputation":"1"},{"iso3":"AFG","country":"Afghanistan","ticker":"food_price_index","full_name":"Food Price Index","units":"LCU, Indexed, January 2018 = 1","food_index_weight":"Laspeyres","data_coverage":"27.7%","r2_imputation":"0.97"},{"iso3":"AFG","country":"Afghanistan","ticker":"fuel_diesel","full_name":"Fuel (diesel)","units":"L","food_index_weight":"0","data_coverage":"52.05%","r2_imputation":"0.98"},{"iso3":"AFG","country":"Afghanistan","ticker":"oil","full_name":"Oil (cooking)","units":"KG","food_index_weight":"1","data_coverage":"26.23%","r2_imputation":"0.96"},{"iso3":"AFG","country":"Afghanistan","ticker":"pulses","full_name":"Pulses","units":"KG","food_index_weight":"1","data_coverage":"26.75%","r2_imputation":"0.96"},{"iso3":"AFG","country":"Afghanistan","ticker":"rice","full_name":"Rice (low quality)","units":"KG","food_index_weight":"0.5","data_coverage":"39.84%","r2_imputation":"0.92"},{"iso3":"AFG","country":"Afghanistan","ticker":"rice_fao","full_name":"Rice (high quality)","units":"Kg","food_index_weight":"0.5","data_coverage":"7.82%","r2_imputation":"0.96"},{"iso3":"AFG","country":"Afghanistan","ticker":"salt","full_name":"Salt","units":"KG","food_index_weight":"1","data_coverage":"25.68%","r2_imputation":"0.97"},{"iso3":"AFG","country":"Afghanistan","ticker":"sugar","full_name":"Sugar","units":"KG","food_index_weight":"1","data_coverage":"25.87%","r2_imputation":"0.97"},{"iso3":"AFG","country":"Afghanistan","ticker":"wage_non_qualified_labour_non_agricultural","full_name":"Wage (non-qualified labour, non-agricultural)","units":"Day","food_index_weight":"0","data_coverage":"32.1%","r2_imputation":"0.98"},{"iso3":"AFG","country":"Afghanistan","ticker":"wage_qualified_labour","full_name":"Wage (qualified labour)","units":"Day","food_index_weight":"0","data_coverage":"31.63%","r2_imputation":"0.99"},{"iso3":"AFG","country":"Afghanistan","ticker":"wheat","full_name":"Wheat","units":"KG","food_index_weight":"1","data_coverage":"40.01%","r2_imputation":"0.95"},{"iso3":"AFG","country":"Afghanistan","ticker":"wheat_flour","full_name":"Wheat flour (high quality)","units":"KG","food_index_weight":"1","data_coverage":"25.78%","r2_imputation":"0.98"},{"iso3":"AFG","country":"Afghanistan","ticker":"wheat_flour_low_price_fao","full_name":"Wheat (flour, low price)","units":"Kg","food_index_weight":"1","data_coverage":"15.18%","r2_imputation":"0.95"}]},"schematype":"survey","data_files":[{"file_id":"AFG_2023_RTFX_MKT","file_name":"AFG_RTFX_mkt_2007_2026-08-24.csv","description":"Monthly price estimates at market\/commodity level","case_count":9676,"var_count":33,"producer":null,"data_checks":null,"version":"2026\/08\/24","notes":null,"metadata":null}],"variables":[{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9440}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9440}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V012","name":"exchange_rate_unofficial","labl":"Exchange rate unofficial","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":3168}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V013","name":"wage_non_qualified_labour_non_agricultural","labl":"Wage non qualified labour non agricultural","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":3219}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V014","name":"wage_qualified_labour","labl":"Wage qualified labour","var_txt":"","var_notes":"","var_sumstat":[{"type":"Number of valid values","value":3219}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V015","name":"o_exchange_rate_unofficial","labl":"Open estimate - Exchange rate unofficial","var_txt":"","var_notes":"o_exchange_rate_unofficial indicates the monthly opening price estimate for the exchange_rate_unofficial. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V016","name":"h_exchange_rate_unofficial","labl":"High estimate - Exchange rate unofficial","var_txt":"","var_notes":"h_exchange_rate_unofficial denotes the highest price achieved by the exchange_rate_unofficial 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V017","name":"l_exchange_rate_unofficial","labl":"Low estimate - Exchange rate unofficial","var_txt":"","var_notes":"l_exchange_rate_unofficial represents the lowest price point for the exchange_rate_unofficial 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V018","name":"c_exchange_rate_unofficial","labl":"Close estimate - Exchange rate unofficial","var_txt":"","var_notes":"c_exchange_rate_unofficial is the closing price estimate for the exchange_rate_unofficial 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V019","name":"inflation_exchange_rate_unofficial","labl":"Inflation - Exchange rate unofficial","var_txt":"","var_notes":"inflation_exchange_rate_unofficial provides the 12-month inflation rate, or price change rate, for exchange_rate_unofficial. 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":9184}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V020","name":"trust_exchange_rate_unofficial","labl":"Trust - Exchange rate unofficial","var_txt":"","var_notes":"trust_exchange_rate_unofficial offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for exchange_rate_unofficial. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V021","name":"o_wage_non_qualified_labour_non_agricultural","labl":"Open estimate - Wage non qualified labour non agricultural","var_txt":"","var_notes":"o_wage_non_qualified_labour_non_agricultural indicates the monthly opening price estimate for the wage_non_qualified_labour_non_agricultural. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V022","name":"h_wage_non_qualified_labour_non_agricultural","labl":"High estimate - Wage non qualified labour non agricultural","var_txt":"","var_notes":"h_wage_non_qualified_labour_non_agricultural denotes the highest price achieved by the wage_non_qualified_labour_non_agricultural 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V023","name":"l_wage_non_qualified_labour_non_agricultural","labl":"Low estimate - Wage non qualified labour non agricultural","var_txt":"","var_notes":"l_wage_non_qualified_labour_non_agricultural represents the lowest price point for the wage_non_qualified_labour_non_agricultural 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V024","name":"c_wage_non_qualified_labour_non_agricultural","labl":"Close estimate - Wage non qualified labour non agricultural","var_txt":"","var_notes":"c_wage_non_qualified_labour_non_agricultural is the closing price estimate for the wage_non_qualified_labour_non_agricultural 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V025","name":"inflation_wage_non_qualified_labour_non_agricultural","labl":"Inflation - Wage non qualified labour non agricultural","var_txt":"","var_notes":"inflation_wage_non_qualified_labour_non_agricultural provides the 12-month inflation rate, or price change rate, for wage_non_qualified_labour_non_agricultural. 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":9184}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V026","name":"trust_wage_non_qualified_labour_non_agricultural","labl":"Trust - Wage non qualified labour non agricultural","var_txt":"","var_notes":"trust_wage_non_qualified_labour_non_agricultural offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for wage_non_qualified_labour_non_agricultural. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V027","name":"o_wage_qualified_labour","labl":"Open estimate - Wage qualified labour","var_txt":"","var_notes":"o_wage_qualified_labour indicates the monthly opening price estimate for the wage_qualified_labour. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V028","name":"h_wage_qualified_labour","labl":"High estimate - Wage qualified labour","var_txt":"","var_notes":"h_wage_qualified_labour denotes the highest price achieved by the wage_qualified_labour 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V029","name":"l_wage_qualified_labour","labl":"Low estimate - Wage qualified labour","var_txt":"","var_notes":"l_wage_qualified_labour represents the lowest price point for the wage_qualified_labour 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V030","name":"c_wage_qualified_labour","labl":"Close estimate - Wage qualified labour","var_txt":"","var_notes":"c_wage_qualified_labour is the closing price estimate for the wage_qualified_labour 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V031","name":"inflation_wage_qualified_labour","labl":"Inflation - Wage qualified labour","var_txt":"","var_notes":"inflation_wage_qualified_labour provides the 12-month inflation rate, or price change rate, for wage_qualified_labour. 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":9184}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V032","name":"trust_wage_qualified_labour","labl":"Trust - Wage qualified labour","var_txt":"","var_notes":"trust_wage_qualified_labour offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for wage_qualified_labour. 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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null},{"file_id":"AFG_2023_RTFX_MKT","vid":"V033","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":9676}],"fid":"AFG_2023_RTFX_MKT","qstn":"","catgry":null}],"variable_groups":[]}