100+ datasets found
  1. China Retail Price Index: Urban: Article for Daily Use

    • ceicdata.com
    Updated Dec 15, 2024
    + more versions
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    CEICdata.com (2024). China Retail Price Index: Urban: Article for Daily Use [Dataset]. https://www.ceicdata.com/en/china/retail-price-index-urban-annual/retail-price-index-urban-article-for-daily-use
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    China
    Variables measured
    Domestic Trade Price
    Description

    China Retail Price Index: Urban: Article for Daily Use data was reported at 101.100 Prev Year=100 in 2022. This records an increase from the previous number of 99.700 Prev Year=100 for 2021. China Retail Price Index: Urban: Article for Daily Use data is updated yearly, averaging 100.399 Prev Year=100 from Dec 1951 (Median) to 2022, with 72 observations. The data reached an all-time high of 117.600 Prev Year=100 in 1951 and a record low of 95.000 Prev Year=100 in 1964. China Retail Price Index: Urban: Article for Daily Use data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Urban: Annual.

  2. T

    United States Consumer Price Index (CPI)

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Consumer Price Index (CPI) [Dataset]. https://tradingeconomics.com/united-states/consumer-price-index-cpi
    Explore at:
    xml, csv, excel, jsonAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1950 - Jun 30, 2025
    Area covered
    United States
    Description

    Consumer Price Index CPI in the United States increased to 322.56 points in June from 321.46 points in May of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  3. China Retail Price Index: Hebei: Article for Daily Use

    • ceicdata.com
    Updated Dec 15, 2024
    + more versions
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    CEICdata.com (2024). China Retail Price Index: Hebei: Article for Daily Use [Dataset]. https://www.ceicdata.com/en/china/retail-price-index-hebei/retail-price-index-hebei-article-for-daily-use
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    China
    Variables measured
    Domestic Trade Price
    Description

    Retail Price Index: Hebei: Article for Daily Use data was reported at 100.200 Prev Year=100 in 2022. This records an increase from the previous number of 99.600 Prev Year=100 for 2021. Retail Price Index: Hebei: Article for Daily Use data is updated yearly, averaging 100.815 Prev Year=100 from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 116.000 Prev Year=100 in 1989 and a record low of 98.600 Prev Year=100 in 2004. Retail Price Index: Hebei: Article for Daily Use data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Hebei.

  4. F

    All-Transactions House Price Index for the United States

    • fred.stlouisfed.org
    json
    Updated May 27, 2025
    + more versions
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    (2025). All-Transactions House Price Index for the United States [Dataset]. https://fred.stlouisfed.org/series/USSTHPI
    Explore at:
    jsonAvailable download formats
    Dataset updated
    May 27, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q1 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

  5. Consumer Price Index 2024 - West Bank and Gaza

    • pcbs.gov.ps
    Updated Feb 19, 2025
    + more versions
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    Palestinian Central Bureau of Statistics (2025). Consumer Price Index 2024 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/732
    Explore at:
    Dataset updated
    Feb 19, 2025
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2024
    Area covered
    West Bank, Palestine
    Description

    Abstract

    The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

    Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

    Geographic coverage

    Palestine West Bank Gaza Strip Jerusalem

    Analysis unit

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Universe

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

    Sampling deviation

    Not apply

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

    In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

    Cleaning operations

    The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

    At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

    Response rate

    Not apply

    Sampling error estimates

    The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

    Data appraisal

    Other technical procedures to improve data quality: Seasonal adjustment processes and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

  6. G

    Daily Wholesale Prices Report

    • canwin-datahub.ad.umanitoba.ca
    • open.canada.ca
    csv, html, json, xml
    Updated Jan 31, 2025
    + more versions
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    Agriculture and Agri-Food Canada (2025). Daily Wholesale Prices Report [Dataset]. https://canwin-datahub.ad.umanitoba.ca/data/dataset/920bc8e2-de26-4bf6-ac41-ed47962d0ff6
    Explore at:
    html, json, xml, csvAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Agriculture and Agri-Food Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Provides daily ranges of domestic and imported horticultural commodities offered for sale. All quoted prices are supplied by a select surveyed group of wholesalers operating in that specific market. The price quoted represent the wholesalers 'asking price' to the retail level for a commodity and does not represent any arrangements or deals. The information provides for commodities, varieties, origins, pack weight or count and price range.

  7. F

    Consumer Price Index for All Urban Consumers: Food in U.S. City Average

    • fred.stlouisfed.org
    json
    Updated Jul 15, 2025
    + more versions
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    (2025). Consumer Price Index for All Urban Consumers: Food in U.S. City Average [Dataset]. https://fred.stlouisfed.org/series/CPIUFDSL
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 15, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for Consumer Price Index for All Urban Consumers: Food in U.S. City Average (CPIUFDSL) from Jan 1947 to Jun 2025 about urban, food, consumer, CPI, inflation, price index, indexes, price, and USA.

  8. Daily LuxX Price Index on Luxembourg Stock Exchange 2019-2023

    • statista.com
    Updated Jul 7, 2025
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    Statista (2025). Daily LuxX Price Index on Luxembourg Stock Exchange 2019-2023 [Dataset]. https://www.statista.com/statistics/1105428/daily-luxx-price-index-on-luxembourg-stock-exchange/
    Explore at:
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jul 1, 2019 - Jan 18, 2023
    Area covered
    Luxembourg
    Description

    The LuxX Price Index, the index of the nine biggest stocks on the Luxembourg Stock Exchange, saw its value decrease by over *** points between February and March, 2020, due to economic uncertainties following the coronavirus pandemic. Since then the index has fluctuated significantly, reaching ******* points as of January 18, 2023 - above the values recorded in February 2020 of around ***** points.

    Luxembourg is known to be an internationally minded financial hub. Of all banks located in the Grand Duchy, for example, only eight are from the country itself. When looking at the number of banks per country of origin, ** come from Germany, with other banking institutions coming from, for example, China, France and Switzerland.

  9. Consumer Price Index 2019 - West Bank and Gaza

    • pcbs.gov.ps
    Updated Dec 26, 2021
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    Palestinian Central Bureau of Statistics (2021). Consumer Price Index 2019 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/704
    Explore at:
    Dataset updated
    Dec 26, 2021
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2019
    Area covered
    West Bank, Palestine
    Description

    Abstract

    The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

    Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

    Geographic coverage

    Palestine West Bank Gaza Strip Jerusalem

    Analysis unit

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Universe

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

    Sampling deviation

    Not apply

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

    In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

    Cleaning operations

    The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

    At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

    Response rate

    Not apply

    Sampling error estimates

    The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

    Data appraisal

    Other technical procedures to improve data quality: Seasonal adjustment processes

  10. h

    Commodities-Daily-Price

    • huggingface.co
    Updated Jun 16, 2024
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    Papers With Backtest (2024). Commodities-Daily-Price [Dataset]. https://huggingface.co/datasets/paperswithbacktest/Commodities-Daily-Price
    Explore at:
    Dataset updated
    Jun 16, 2024
    Dataset authored and provided by
    Papers With Backtest
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    Dataset Information

    This dataset includes daily price data for various commodities.

      Instruments Included
    

    BDIY: Baltic Dry Index BEEF: Beef (dollars per pound) BIT: Bitumen (dollars per metric ton) C1: Corn (dollars per bushel) CC1: Cocoa (dollars per metric ton) CHE: Cheese (dollars per pound) CL1: Crude Oil (dollars per barrel) CO1: Brent Crude Oil (dollars per barrel) CRYTR: CRB Index CT1: Cotton (cents per pound) DA: Milk (dollars per hundredweight) DL1: Ethanol… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Commodities-Daily-Price.

  11. F

    Producer Price Index by Commodity for Advertising Space and Time Sales:...

    • fred.stlouisfed.org
    json
    Updated Oct 15, 2015
    + more versions
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    (2015). Producer Price Index by Commodity for Advertising Space and Time Sales: Advertising Space Sales in Newspapers, Daily and Sunday Papers (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/WPU36110201
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Oct 15, 2015
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Producer Price Index by Commodity for Advertising Space and Time Sales: Advertising Space Sales in Newspapers, Daily and Sunday Papers (DISCONTINUED) (WPU36110201) from Jun 1999 to Sep 2015 about periodicals, advertisement, sales, commodities, PPI, inflation, price index, indexes, price, and USA.

  12. T

    United States FHFA House Price Index

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States FHFA House Price Index [Dataset]. https://tradingeconomics.com/united-states/housing-index
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1991 - May 31, 2025
    Area covered
    United States
    Description

    Housing Index in the United States decreased to 434.40 points in May from 435.10 points in April of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  13. T

    Baltic Exchange Dry Index - Price Data

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 2, 2025
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    TRADING ECONOMICS (2025). Baltic Exchange Dry Index - Price Data [Dataset]. https://tradingeconomics.com/commodity/baltic
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    Aug 2, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 4, 1985 - Aug 1, 2025
    Area covered
    World
    Description

    Baltic Dry rose to 2,018 Index Points on August 1, 2025, up 0.75% from the previous day. Over the past month, Baltic Dry's price has risen 39.85%, and is up 20.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on August of 2025.

  14. Consumer Price Index 2023 - West Bank and Gaza

    • pcbs.gov.ps
    Updated Mar 10, 2024
    + more versions
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    Palestinian Central Bureau of Statistics (2024). Consumer Price Index 2023 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/729
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    Dataset updated
    Mar 10, 2024
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2023
    Area covered
    West Bank, Palestine
    Description

    Abstract

    The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

    Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

    Geographic coverage

    Palestine West Bank Gaza Strip Jerusalem

    Analysis unit

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Universe

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

    Sampling deviation

    Not apply

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

    In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

    Cleaning operations

    The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

    At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

    Response rate

    Not apply

    Sampling error estimates

    The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

    Data appraisal

    Other technical procedures to improve data quality: Seasonal adjustment processes and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

  15. China CPI: Urban: HA: Daily Use Household Article

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China CPI: Urban: HA: Daily Use Household Article [Dataset]. https://www.ceicdata.com/en/china/consumer-price-index-urban-monthly/cpi-urban-ha-daily-use-household-article
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2015 - Dec 1, 2015
    Area covered
    China
    Variables measured
    Consumer Prices
    Description

    China Consumer Price Index (CPI): Urban: HA: Daily Use Household Article data was reported at 100.700 Prev Year=100 in Dec 2015. This stayed constant from the previous number of 100.700 Prev Year=100 for Nov 2015. China Consumer Price Index (CPI): Urban: HA: Daily Use Household Article data is updated monthly, averaging 101.100 Prev Year=100 from Jan 2005 (Median) to Dec 2015, with 132 observations. The data reached an all-time high of 107.100 Prev Year=100 in Nov 2008 and a record low of 99.300 Prev Year=100 in Feb 2010. China Consumer Price Index (CPI): Urban: HA: Daily Use Household Article data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IA: Consumer Price Index: Urban: Same Month PY=100.

  16. F

    Producer Price Index by Commodity: All Commodities

    • fred.stlouisfed.org
    json
    Updated Jul 16, 2025
    + more versions
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    (2025). Producer Price Index by Commodity: All Commodities [Dataset]. https://fred.stlouisfed.org/series/PPIACO
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    jsonAvailable download formats
    Dataset updated
    Jul 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to Jun 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

  17. Inflation Nowcasting

    • clevelandfed.org
    json
    Updated Mar 10, 2017
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    Federal Reserve Bank of Cleveland (2017). Inflation Nowcasting [Dataset]. https://www.clevelandfed.org/indicators-and-data/inflation-nowcasting
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    jsonAvailable download formats
    Dataset updated
    Mar 10, 2017
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Federal Reserve Bank of Cleveland provides daily “nowcasts” of inflation for two popular price indexes, the price index for personal consumption expenditures (PCE) and the Consumer Price Index (CPI). These nowcasts give a sense of where inflation is today. Released each business day.

  18. Highest daily TOPIX closing prices 1980-2024

    • statista.com
    Updated Jan 9, 2025
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    Statista (2025). Highest daily TOPIX closing prices 1980-2024 [Dataset]. https://www.statista.com/statistics/1538036/japan-tokyo-stock-price-index-highest-daily-closing-prices/
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    Dataset updated
    Jan 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Japan
    Description

    In 2024, the Tokyo Stock Price Index (TOPIX) reached a daily closing high of ******** points on July 11, the highest daily closing price recorded since 1989. TOPIX is a free-float adjusted market capitalization-weighted index that has been published by the Tokyo Stock Exchange (TSE) since 1969. The market capitalization as of the base date (January 4, 1968) is set at 100 points.

  19. Daily stock price indexes of selected technology firms 2020-2025

    • statista.com
    Updated Jun 25, 2025
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    Statista (2025). Daily stock price indexes of selected technology firms 2020-2025 [Dataset]. https://www.statista.com/statistics/1343774/daily-stock-price-indexes-of-selected-technology-companies/
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    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 6, 2020 - Feb 3, 2025
    Area covered
    Worldwide
    Description

    This statistic shows the stock price development of selected companies in the technology industry from January 6, 2020 to February 3, 2025. During this period, stock prices of most of the tech companies have increased. Out of all companies shown here, stock values of **** saw the most substantial increase between January and October 2020. In February 3, 2025, ***** stock prices increased more than others with over an increase of *** index points.

  20. T

    Eggs US - Price Data

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 1, 2025
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    TRADING ECONOMICS (2025). Eggs US - Price Data [Dataset]. https://tradingeconomics.com/commodity/eggs-us
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    excel, csv, xml, jsonAvailable download formats
    Dataset updated
    Aug 1, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    May 25, 2012 - Aug 1, 2025
    Area covered
    World
    Description

    Eggs US fell to 2.92 USD/Dozen on August 1, 2025, down 3.71% from the previous day. Over the past month, Eggs US's price has risen 14.59%, and is up 6.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Share
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Close
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CEICdata.com (2024). China Retail Price Index: Urban: Article for Daily Use [Dataset]. https://www.ceicdata.com/en/china/retail-price-index-urban-annual/retail-price-index-urban-article-for-daily-use
Organization logo

China Retail Price Index: Urban: Article for Daily Use

Explore at:
Dataset updated
Dec 15, 2024
Dataset provided by
CEIC Data
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Dec 1, 2011 - Dec 1, 2022
Area covered
China
Variables measured
Domestic Trade Price
Description

China Retail Price Index: Urban: Article for Daily Use data was reported at 101.100 Prev Year=100 in 2022. This records an increase from the previous number of 99.700 Prev Year=100 for 2021. China Retail Price Index: Urban: Article for Daily Use data is updated yearly, averaging 100.399 Prev Year=100 from Dec 1951 (Median) to 2022, with 72 observations. The data reached an all-time high of 117.600 Prev Year=100 in 1951 and a record low of 95.000 Prev Year=100 in 1964. China Retail Price Index: Urban: Article for Daily Use data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Urban: Annual.

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