<_value> NADA Monthly food price estimates by product and market Bangladesh, 73 markets, 2007/01/01-2024/01/01, version 2024-01-25 Real Time Food Prices BGD_2021_RTFP_v02_M Bo Pieter Johannes Andrée

World Food Programme (WFP)

Food and Agriculture Organization (FAO)

MetadataEditor Foreign, Commonwealth & Development Office Foreign, Commonwealth & Development Office Department of Foreign Affairs and Trade Department of Foreign Affairs and Trade Federal Ministry for Economic Cooperation and Development as part of the World Bank’s Food Systems 2030 Multi-Donor Trust Fund Federal Ministry for Economic Cooperation and Development as part of the World Bank’s Food Systems 2030 Multi-Donor Trust Fund KP-P174529-KMCE-TF0B4149 TF0B6892 TF0B6579 TF073570 TF0C0728 Monthly food price estimates in fragile countries FCV Data Platform Food Price Monitor FPM Real Time Food Prices RTFP Inflation Food Price Inflation Food security Food Insecurity Food Crisis Famine Fragility FCS FCV Food Price Crisis Commodity prices Maize Sorghum Wheat Rice Agricultural prices Food prices Real Time Food Prices Price measurement Real-time exchange rate data Commodity items Geo-referenced market locations Sub-national price inflation series Consumer Price Inflation (CPI) National-level CPI indicators International Comparison Program (ICP) Inflation series Market prices Price analysis Real-time data National prices International prices Price fluctuations Price trends Price Volatility Bangladesh Bangladesh The data cover the following sub-national areas: Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Market Average Sub-national level, Admin 2 (selected) World Food Programme (WFP) https://data.humdata.org/organization/wfp?vocab_Topics=prices World Food Programme (WFP); data published in the Humanitarian Data Exchange (HDX) data catalog at https://data.humdata.org/ Food and Agriculture Organization (FAO) https://fpma.fao.org/giews/fpmat4/#/dashboard/tool/domestic Local exchange rates and implied exchange rates data derived from commodity pairs published by FAO through the Food Price Monitoring and Analysis (FMPA) Tool World Bank Microdata Library, FCV Collection The estimates presented in this dataset are all based on publicly-available data from the World Food Programme. The dataset of price estimates is published as open data. Data Help Desk BGD_RTFP_mkt_2007_2024-01-25.csv Monthly price estimates at market/commodity level (all available countries) 15170 130 2024/01/25 Country code 15170 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. Country 15170 Country names follow their appearance in the World Bank World Development Indicators. Area name (admin. level 1) 15170 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. Area name (admin. level 2) 15170 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. Market name 15170 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. Latitude 14965 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. Longitude 14965 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. Market location identifier 15170 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. Year 15170 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. Month 15170 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. Currency 15170 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. Components 15170 Start dense data 15170 Last survey point 15170 Data coverage 15170 Data coverage recent 15170 Index confidence score 15170 Spatial interpolation (0/1) 15170 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. Beans Cabbage Carrots Eggs Garlic Lentils 2489 Meat beef chops Meat chicken whole Meat pork Oil 3005 Onions Potatoes Rice 2354 Tomatoes Wheat flour 2548 Open estimate - Beans 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. High estimate - Beans 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. Low estimate - Beans 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. Close estimate - Beans 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. Inflation - Beans 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. Trust - Beans 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. Open estimate - Cabbage o_cabbage indicates the monthly opening price estimate for the commodity cabbage. 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. High estimate - Cabbage h_cabbage denotes the highest price achieved by the commodity cabbage 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. Low estimate - Cabbage l_cabbage represents the lowest price point for the commodity cabbage 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. Close estimate - Cabbage c_cabbage is the closing price estimate for the commodity cabbage 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. Inflation - Cabbage inflation_cabbage provides the 12-month inflation rate, or price change rate, for commodity cabbage. 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. Trust - Cabbage trust_cabbage offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for cabbage. 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. Open estimate - Carrots 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. High estimate - Carrots 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. Low estimate - Carrots 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. Close estimate - Carrots 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. Inflation - Carrots 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. Trust - Carrots 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. Open estimate - Eggs 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. High estimate - Eggs 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. Low estimate - Eggs 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. Close estimate - Eggs 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. Inflation - Eggs 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. Trust - Eggs 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. Open estimate - Garlic o_garlic indicates the monthly opening price estimate for the commodity garlic. 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. High estimate - Garlic h_garlic denotes the highest price achieved by the commodity garlic 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. Low estimate - Garlic l_garlic represents the lowest price point for the commodity garlic 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. Close estimate - Garlic c_garlic is the closing price estimate for the commodity garlic 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. Inflation - Garlic inflation_garlic provides the 12-month inflation rate, or price change rate, for commodity garlic. 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. Trust - Garlic trust_garlic offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for garlic. 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. Open estimate - Lentils 15170 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. High estimate - Lentils 15170 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. Low estimate - Lentils 15170 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. Close estimate - Lentils 15170 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. Inflation - Lentils 14282 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. Trust - Lentils 15170 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. Open estimate - Meat beef chops o_meat_beef_chops indicates the monthly opening price estimate for the commodity meat_beef_chops. 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. High estimate - Meat beef chops h_meat_beef_chops denotes the highest price achieved by the commodity meat_beef_chops 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. Low estimate - Meat beef chops l_meat_beef_chops represents the lowest price point for the commodity meat_beef_chops 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. Close estimate - Meat beef chops c_meat_beef_chops is the closing price estimate for the commodity meat_beef_chops 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. Inflation - Meat beef chops inflation_meat_beef_chops provides the 12-month inflation rate, or price change rate, for commodity meat_beef_chops. 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. Trust - Meat beef chops trust_meat_beef_chops offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for meat_beef_chops. 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. Open estimate - Meat chicken whole o_meat_chicken_whole indicates the monthly opening price estimate for the commodity meat_chicken_whole. 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. High estimate - Meat chicken whole h_meat_chicken_whole denotes the highest price achieved by the commodity meat_chicken_whole 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. Low estimate - Meat chicken whole l_meat_chicken_whole represents the lowest price point for the commodity meat_chicken_whole 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. Close estimate - Meat chicken whole c_meat_chicken_whole is the closing price estimate for the commodity meat_chicken_whole 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. Inflation - Meat chicken whole inflation_meat_chicken_whole provides the 12-month inflation rate, or price change rate, for commodity meat_chicken_whole. 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. Trust - Meat chicken whole trust_meat_chicken_whole offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for meat_chicken_whole. 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. Open estimate - Meat pork o_meat_pork indicates the monthly opening price estimate for the commodity meat_pork. 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. High estimate - Meat pork h_meat_pork denotes the highest price achieved by the commodity meat_pork 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. Low estimate - Meat pork l_meat_pork represents the lowest price point for the commodity meat_pork 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. Close estimate - Meat pork c_meat_pork is the closing price estimate for the commodity meat_pork 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. Inflation - Meat pork inflation_meat_pork provides the 12-month inflation rate, or price change rate, for commodity meat_pork. 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. Trust - Meat pork trust_meat_pork offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for meat_pork. 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. Open estimate - Oil 15170 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. High estimate - Oil 15170 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. Low estimate - Oil 15170 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. Close estimate - Oil 15170 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. Inflation - Oil 14282 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. Trust - Oil 15170 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. Open estimate - Onions o_onions indicates the monthly opening price estimate for the commodity onions. 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. High estimate - Onions h_onions denotes the highest price achieved by the commodity onions 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. Low estimate - Onions l_onions represents the lowest price point for the commodity onions 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. Close estimate - Onions c_onions is the closing price estimate for the commodity onions 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. Inflation - Onions inflation_onions provides the 12-month inflation rate, or price change rate, for commodity onions. 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. Trust - Onions trust_onions offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for onions. 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. Open estimate - Potatoes 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. High estimate - Potatoes 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. Low estimate - Potatoes 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. Close estimate - Potatoes 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. Inflation - Potatoes 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. Trust - Potatoes 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. Open estimate - Rice 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. High estimate - Rice 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. Low estimate - Rice 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. Close estimate - Rice 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. Inflation - Rice 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. Trust - Rice 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. Open estimate - Tomatoes 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. High estimate - Tomatoes 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. Low estimate - Tomatoes 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. Close estimate - Tomatoes 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. Inflation - Tomatoes 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. Trust - Tomatoes 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. Open estimate - Wheat flour 15170 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. High estimate - Wheat flour 15170 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. Low estimate - Wheat flour 15170 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. Close estimate - Wheat flour 15170 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. Inflation - Wheat flour 14282 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. Trust - Wheat flour 15170 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. Open estimate - Food price index 15170 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. High estimate - Food price index 15170 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. Low estimate - Food price index 15170 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. Close estimate - Food price index 15170 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. Inflation - Food price index 14282 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. Trust - Food price index 15170 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. Date in yyyy-mm-dd format 15170 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.