13 datasets found
  1. Replication dataset and calculations for PIIE PB 19-16, Average Inflation...

    • piie.com
    Updated Nov 4, 2019
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    David Reifschneider; David Wilcox (2019). Replication dataset and calculations for PIIE PB 19-16, Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, by David Reifschneider and David Wilcox. (2019). [Dataset]. https://www.piie.com/publications/policy-briefs/average-inflation-targeting-would-be-weak-tool-fed-deal-recession-and
    Explore at:
    Dataset updated
    Nov 4, 2019
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    David Reifschneider; David Wilcox
    Description

    This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, PIIE Policy Brief 19-16. If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2019). Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation. PIIE Policy Brief 19-16. Peterson Institute for International Economics.

  2. F

    Dates of U.S. recessions as inferred by GDP-based recession indicator

    • fred.stlouisfed.org
    json
    Updated Apr 30, 2025
    + more versions
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    (2025). Dates of U.S. recessions as inferred by GDP-based recession indicator [Dataset]. https://fred.stlouisfed.org/series/JHDUSRGDPBR
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    jsonAvailable download formats
    Dataset updated
    Apr 30, 2025
    License

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

    Description

    Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q4 2024 about recession indicators, GDP, and USA.

  3. U.S. monthly projected recession probability 2021-2026

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). U.S. monthly projected recession probability 2021-2026 [Dataset]. https://www.statista.com/statistics/1239080/us-monthly-projected-recession-probability/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 2021 - Apr 2026
    Area covered
    United States
    Description

    By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

  4. g

    Harris 1969 Inflation and Economic Survey, study no. 1939

    • datasearch.gesis.org
    • dataverse-staging.rdmc.unc.edu
    Updated Jan 22, 2020
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    Louis Harris and Associates, Inc. (2020). Harris 1969 Inflation and Economic Survey, study no. 1939 [Dataset]. https://datasearch.gesis.org/dataset/httpsdataverse.unc.eduoai--hdl1902.29H-1939
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    Dataset updated
    Jan 22, 2020
    Dataset provided by
    Odum Institute Dataverse Network
    Authors
    Louis Harris and Associates, Inc.
    Description

    Survey investigates indepth attitudes toward the economy, affects of inflation, and financial management.

    Questions include standard of living, income, financial stress, savings and borrowing habits, money budgeted for housing, transportation, food and leisure. Questions about taxation, causes of inflation, recession, tax revolt, cost of living and financial investments are also included. A tack-on surveys attitudes toward environmental air and water pollution, political affairs, civil rights, and Joe Namath.

  5. Consumer Price Index 2021 - West Bank and Gaza

    • pcbs.gov.ps
    Updated May 18, 2023
    + more versions
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    Palestinian Central Bureau of Statistics (2023). Consumer Price Index 2021 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/711
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    Dataset updated
    May 18, 2023
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2021
    Area covered
    West Bank, Gaza, Gaza Strip
    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

  6. g

    Kwalitatieve analyse: kunst én kunde - dataset bron 15. "Larry Elliott - A...

    • datasearch.gesis.org
    • ssh.datastations.nl
    • +1more
    Updated Jan 23, 2020
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    Evers, drs. J.C. (Erasmus University Rotterdam/Evers Research & training) DAI=info:eu-repo/dai/nl/074934716 (2020). Kwalitatieve analyse: kunst én kunde - dataset bron 15. "Larry Elliott - A double-dip recession?" [Dataset]. http://doi.org/10.17026/dans-xej-bpb4
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    Dataset updated
    Jan 23, 2020
    Dataset provided by
    DANS (Data Archiving and Networked Services)
    Authors
    Evers, drs. J.C. (Erasmus University Rotterdam/Evers Research & training) DAI=info:eu-repo/dai/nl/074934716
    Description

    Formaat: PDF Omvang: 70Kb

    This article was published on the Guardian website at 19.00 BST on Tuesday 16 June 2009. It was last modified at 13.43 BST on Tuesday 19 August 2014. Online beschikbaar: http://www.theguardian.com/commentisfree/2009/jun/16/deflation-double-dip-recession-inflation/print [01-12-2014] © 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

  7. J

    Probabilistic forecasting of output growth, inflation and the balance of...

    • journaldata.zbw.eu
    txt, zip
    Updated Dec 7, 2022
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    Matthew Greenwood-Nimmo; Viet Hoang Nguyen; Yongcheol Shin; Matthew Greenwood-Nimmo; Viet Hoang Nguyen; Yongcheol Shin (2022). Probabilistic forecasting of output growth, inflation and the balance of trade in a GVAR framework (replication data) [Dataset]. http://doi.org/10.15456/jae.2022320.0725005726
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    zip(268656), txt(8197)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Matthew Greenwood-Nimmo; Viet Hoang Nguyen; Yongcheol Shin; Matthew Greenwood-Nimmo; Viet Hoang Nguyen; Yongcheol Shin
    License

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

    Description

    We apply a global vector autoregressive (GVAR) model to the analysis of inflation, output growth and global imbalances among a group of 33 countries (26 regions). We account for structural instability by use of country-specific intercept shifts, the timings of which are identified taking into account both statistical evidence and our knowledge of historic economic conditions and events. Using this model, we compute both central forecasts and scenario-based probabilistic forecasts for a range of events of interest, including the sign and trajectory of the balance of trade, the achievement of a short-term inflation target, and the incidence of recession and slow growth. The forecasting performance of the GVAR model in relation to the ongoing financial crisis is quite remarkable. It correctly identifies a pronounced and widespread economic contraction accompanied by a marked shift in the net trade balance of the Eurozone and Japan. Moreover, this promising out-of-sample forecasting performance is substantiated by a raft of statistical tests which indicate that the predictive accuracy of the GVAR model is broadly comparable to that of standard benchmark models over short horizons and superior over longer horizons. Hence we conclude that GVAR models may be a useful forecasting tool for institutions operating at both the national and supra-national levels.

  8. J

    Dynamic factor model with infinite‐dimensional factor space: Forecasting...

    • journaldata.zbw.eu
    Updated Dec 7, 2022
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    Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi; Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi (2022). Dynamic factor model with infinite‐dimensional factor space: Forecasting (replication data) [Dataset]. http://doi.org/10.15456/jae.2022326.0709410218
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    csv(530182), application/vnd.wolfram.mathematica.package(1930), application/vnd.wolfram.mathematica.package(2584), xlsx(666456), csv(558188), txt(1821), xlsx(685206)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi; Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi
    License

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

    Description

    The paper compares the pseudo real-time forecasting performance of three dynamic factor models: (i) the standard principal component model introduced by Stock and Watson in 2002; (ii) the model based on generalized principal components, introduced by Forni, Hallin, Lippi, and Reichlin in 2005; (iii) the model recently proposed by Forni, Hallin, Lippi, and Zaffaroni in 2015. We employ a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery (an update of the so-called Stock and Watson dataset). Using a rolling window for estimation and prediction, we find that model (iii) significantly outperforms models (i) and (ii) in the Great Moderation period for both industrial production and inflation, and that model (iii) is also the best method for inflation over the full sample. However, model (iii) is outperformed by models (ii) and (i) over the full sample for industrial production.

  9. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jul 11, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
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    jsonAvailable download formats
    Dataset updated
    Jul 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

  10. U

    Harris 1972 Presidential Election and Economic Outlook Survey, study no....

    • dataverse-staging.rdmc.unc.edu
    • dataverse.unc.edu
    Updated May 2, 2008
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    UNC Dataverse (2008). Harris 1972 Presidential Election and Economic Outlook Survey, study no. 2235 [Dataset]. https://dataverse-staging.rdmc.unc.edu/dataset.xhtml?persistentId=hdl:1902.29/H-2235
    Explore at:
    application/x-sas-transport(3629040), tsv(901613), pdf(1151938), bin(1514880), text/x-sas-syntax(88386), application/x-spss-por(931356)Available download formats
    Dataset updated
    May 2, 2008
    Dataset provided by
    UNC Dataverse
    License

    https://dataverse-staging.rdmc.unc.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=hdl:1902.29/H-2235https://dataverse-staging.rdmc.unc.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=hdl:1902.29/H-2235

    Description

    Pre-election survey investigates reasons for preferences for Richard Nixon or George McGovern for president.Additional questions focus on the economy, inflation, recession, unemployment, Wage-Price Control Board, break-in at Democratic National Headquarters, and legitimacy of wiretapping.

  11. T

    Germany GDP Growth Rate

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 23, 2025
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    TRADING ECONOMICS (2025). Germany GDP Growth Rate [Dataset]. https://tradingeconomics.com/germany/gdp-growth
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    csv, json, excel, xmlAvailable download formats
    Dataset updated
    May 23, 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
    Jun 30, 1970 - Mar 31, 2025
    Area covered
    Germany
    Description

    The Gross Domestic Product (GDP) in Germany expanded 0.40 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - Germany GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  12. T

    India GDP Annual Growth Rate

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 30, 2025
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    TRADING ECONOMICS (2025). India GDP Annual Growth Rate [Dataset]. https://tradingeconomics.com/india/gdp-growth-annual
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset updated
    May 30, 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
    Dec 31, 1951 - Mar 31, 2025
    Area covered
    India
    Description

    The Gross Domestic Product (GDP) in India expanded 7.40 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - India GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  13. T

    Australia GDP Growth Rate

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 4, 2025
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    TRADING ECONOMICS (2025). Australia GDP Growth Rate [Dataset]. https://tradingeconomics.com/australia/gdp-growth
    Explore at:
    csv, json, xml, excelAvailable download formats
    Dataset updated
    Jun 4, 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
    Dec 31, 1959 - Mar 31, 2025
    Area covered
    Australia
    Description

    The Gross Domestic Product (GDP) in Australia expanded 0.20 percent in the first quarter of 2025 over the previous quarter. This dataset provides - Australia GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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David Reifschneider; David Wilcox (2019). Replication dataset and calculations for PIIE PB 19-16, Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, by David Reifschneider and David Wilcox. (2019). [Dataset]. https://www.piie.com/publications/policy-briefs/average-inflation-targeting-would-be-weak-tool-fed-deal-recession-and
Organization logo

Replication dataset and calculations for PIIE PB 19-16, Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, by David Reifschneider and David Wilcox. (2019).

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Nov 4, 2019
Dataset provided by
Peterson Institute for International Economicshttp://www.piie.com/
Authors
David Reifschneider; David Wilcox
Description

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, PIIE Policy Brief 19-16. If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2019). Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation. PIIE Policy Brief 19-16. Peterson Institute for International Economics.

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