GDP Deflator in the United States increased to 127.43 points in the first quarter of 2025 from 126.26 points in the fourth quarter of 2024. This dataset provides the latest reported value for - United States GDP Deflator - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The dataset represents the joint dynamics of Financial Stress Index (FSI), Consumer Price Index (CPI) calculated and provided by the National Bank of Ukraine (NBU) and Gross Domestic Product (GDP) provided by SSSU for Ukraine.

The monthly dataset range is Feb 2004-Feb 2022, the effective balanced range is Jan 2011-Dec 2021.

The daily FSI data is aggregated into monthly series as a period average. The CPI series are monthly. The quarterly GDP data is seasonally adjusted and interpolated into monthly data with the use of ARIMA model and cubic spline method accordingly, converted into year-over-year series (dGDP).

Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

Contains values for American Consumer Price Index, M2 Money Supply, and Real GDP from 1800 to 2008. Data taken from the following sources:

Historical Statistics of the United States, Colonial Times to 1970

https://www.census.gov/library/publications/1975/compendia/hist_stats_colonial-1970.html

Maddison Project Database 2020

https://www.rug.nl/ggdc/historicaldevelopment/maddison/?lang=en

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data was collected from the FRED website.

Contains economic indicators often associated with recessions along with recession status data. Data collected on smallest time unit and earliest time date available for each indicator which results in many nulls but increased flexibility for the users of this dataset.

• recession: "1" recessionary period, "0" non-recessionary period (Monthly)
• cpi: CPI (1982-1984=INDEX 100) (Monthly)
• gdp: Real GDP Billions of Chained 2017 Dollars (Quarterly)
• unemployment: Unemployment Rate (Monthly)
• m2: M2 Billions of Dollars (Monthly)
• fed_funds: Federal Funds Rate (Monthly)
• ten_two: 10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity (Monthly)
• residential: Real Residential Property Price Rate (Quarterly)

Comprehensive description of each variable can be found at https://fred.stlouisfed.org/

Analysis of ‘Extra Datasets for TPS Jan 2022| GDP-CPI-Holidays’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sardorabdirayimov/extra-datasets-for-tps-jan-2022-gdpcpiholidays on 28 January 2022.

--- Dataset description provided by original source is as follows ---

I edited several datasets in Kaggle and make them more user-friendly. Current Datasets are very convenient to add them to main dataset by single pandas method | merge(on=['date'|'year', 'country']) Example notebook: https://www.kaggle.com/sardorabdirayimov/adding-extra-datasets-to-main Good luck on competition!

--- Original source retains full ownership of the source dataset ---

Inflation Rate in India decreased to 2.82 percent in May from 3.16 percent in April of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Description: This dataset contains historical economic data spanning from 1871 to 2024, used in Jaouad Karfali’s research on Economic Cycle Analysis with Numerical Time Cycles. The study aims to improve economic forecasting accuracy through the 9-year cycle model, which demonstrates superior predictive capabilities compared to traditional economic indicators.

Dataset Contents: The dataset includes a comprehensive range of economic indicators used in the research, such as:

USGDP_1871-2024.csv – U.S. Gross Domestic Product (GDP) data. USCPI_cleaned.csv – U.S. Consumer Price Index (CPI), cleaned and processed. USWAGE_1871-2024.csv – U.S. average wages data. EXCHANGEGLOBAL_cleaned.csv – Global exchange rates for the U.S. dollar. EXCHANGEPOUND_cleaned.csv – U.S. dollar to British pound exchange rates. INTERESTRATE_1871-2024.csv – U.S. interest rate data. UNRATE.csv – U.S. unemployment rate statistics. POPTOTUSA647NWDB.csv – U.S. total population data. Significance of the Data: This dataset serves as a foundation for a robust economic analysis of the U.S. economy over multiple decades. It was instrumental in testing the 9-year economic cycle model, which demonstrated an 85% accuracy rate in economic forecasting when compared to traditional models such as ARIMA and VAR.

Applications:

Economic Forecasting: Predicts a 1.5% decline in GDP in 2025, followed by a gradual recovery between 2026-2034. Economic Stability Analysis: Used for comparing forecasts with estimates from institutions like the IMF and World Bank. Academic and Institutional Research: Supports studies in economic cycles and long-term forecasting. Source & Further Information: For more details on the methodology and research findings, refer to the full paper published on SSRN:

https://ssrn.com/author=7429208 https://orcid.org/0009-0002-9626-7289

• Jaouad Karfali

GDP Deflator in Japan increased to 109 points in the fourth quarter of 2023 from 105.10 points in the third quarter of 2023. This dataset provides the latest reported value for - Japan GDP Deflator - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Explore the most important economic variables dataset including Gross Domestic Product, Inflation, Imports, Exports, Population, National Accounts, and more. Analyze economic trends in United Arab Emirates and make informed decisions.

Gross Domestic Product (Million US$), Inflation %, Imports of Goods and Services (cif), Population (Thousand Persons), Exports of Goods and Services (fob), Disposable Income (Million US$), Gross National Income (Million US$), Net National Income (Million US$), National Saving (Million US$), Final Consumption Expenditure (Million US$), Gross Fixed Capital Formation (Million US$), GDP, wages, CPI, Price, ITEM

United Arab Emirates Follow data.kapsarc.org for timely data to advance energy economics research.. 2019 Data is Preliminary.

The dataset contains several macroeconomic time-series regarding the Russian economy. The time-series were collected from the Russian Federal State Statistics Service, the Bank of Russia and Federal Reserve Economic Data. The time-series included in the dataset are: 1. Time: 1-Jan-2005 = 1, every successive step in time represents one quarter 2. Date: Quarterly dates from 1-Jan-2005 to 1-Oct-2021 5. GDP: Quarterly nominal GDP in 2016 prices, excluding seasonal factor (bln RUB) 6. GDPgr: Nominal GDP growth rate (Quarterly, %) 7. M0: Base or high-powered money (bln RUB) 8. M0gr: M0 growth rate (Quarterly, %) 9. BM: M2 measure of money supply (bln RUB) 10. BMgr: M2 growth rate (Quarterly, %) 11. Interest: 90-day interbank rate (APR, %) 12. USDRUB: USD/RUB exchange rate (RUB) 12. EURRUB: EUR/RUB exchange rate (RUB) 13. Unemployment: Unemployment rate (%) 14. PPI: Domestic producer price index (index: 2015=100) 15. PPIgr: Growth rate of producer price index (Quarterly, %) 16. OIL: Spot prices of Brent per barrel (USD) 17. OILgr: Growth rate of Brent prices (Quarterly, %) 18. WAGE: Average monthly nominal wage rate (RUB) 19. WAGEgr: Changes in nominal wage rate (Quarterly, %) 3. CPI: Change in CPI as a ratio (End of quarter to end of previous quarter, %) 4. Inflation: Percentage change in CPI, calculated as Relative CPI - 100 (Quarterly, %)

The data was used to in time-series regression modelling to explain the factors affecting inflation in Russia. Some other modelling ideas for the dataset are: 1. Shift the focus from factor analysis to predicting future inflation 2. Perform factor analyses of other key macroeconomic variables, such as the GDP growth rate, the unemployment rate or the interest rate

Due to the low number of available observations because of quarterly sampling, this dataset is probably better suited to time-series econometric analysis rather than more modern machine learning methods.

Comprehensive database of time series covering measures of inflation data for the UK including CPIH, CPI and RPI.

We present a new, publicly available database of real-time data and forecasts from the Bank of Canada's staff economic projections, which will be updated on an annual basis. We describe the data construct, its variables, coverage, and frequency. We then provide a forecast evaluation for gross domestic product (GDP) growth, consumer price index (CPI) inflation and the policy rate since 1982: We compare the staff's forecasts with those from commonly used time series models estimated with the real-time data, and with forecasts from other professional forecasters, and provide standard bias tests. Finally, we study changes in predictability of the Canadian economy following the announcement of the inflation-targeting regime in 1991. Our data set is unprecedented outside the USA, and our evidence is particularly interesting, as it covers over 30 years of staff forecasts, two severe recessions, and different monetary policy regimes.

This is the data used for the estimation of the GVAR model as in "China's Emergence in the World Economy and Business Cycles in Latin America" (access the study in the related URL Section). The dataset includes quarterly data for twenty-five major advanced and emerging economies plus the euro area, covering more than 90 percent of world GDP. The variables included in the dataset are real GDP, CPI inflation, real equity prices, real exchange rates, short-term and long-term interest rates, and the price of oil. Updates of this dataset -together with the baseline GVAR code- can be found in the Related URL section below. Years covered: 1979 - 2009.

Inflation Rate in Vietnam increased to 3.57 percent in June from 3.24 percent in May of 2025. This dataset provides the latest reported value for - Vietnam Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The variables included in the dataset are real GDP (seasonally adjusted, in log-levels, https://sdw.ecb.de/quickview.do?SERIES_KEY=314.MNA.Q.Y.AT.W2.S1.S1.B.B1GQ._Z._Z._Z.EUR.LR.N), the GDP Deflator (seasonally adjusted, in log-levels, https://data.ecb.europa.eu/data/datasets/MNA/MNA.Q.Y.AT.W2.S1.S1.B.B1GQ._Z._Z._Z.IX.D.N), CPI (food and energy excluded, base year 2015, seasonally adjusted, enters in log-levels, https://www.oecd.org/en/data/indicators/inflation-cpi.html}{retrieved from OECD Data Archive), the EUR/USD exchange rate (https://data.ecb.europa.eu/data/datasets/EXR/EXR.D.USD.EUR.SP00.A), a measure of bank concentration by country (interpolated to a quarterly series from yearly values, only contemporaneous values included, https://data.ecb.europa.eu/data/datasets/SSI/SSI.A.AT.122C.H10.X.A1.Z0Z.Z) the cost of new short-term (https://data.ecb.europa.eu/data/datasets/MIR/MIR.M.U2.B.A2J.FM.R.A.2230.EUR.N) and long-term (https://data.ecb.europa.eu/data/datasets/MIR/MIR.M.U2.B.A2J.KM.R.A.2230.EUR.N) borrowing in the euro area, the monetary policy shocks as in Altavilla et al. (2019) (https://doi.org/10.1016/j.jmoneco.2019.08.016), which were summed up to quarterly values, and finally the loans granted by Euro Area Monetary Financial Institutions to domestic non financial corporations (https://data.ecb.europa.eu/data/datasets/QSA/QSA.Q.N.AT.W2.S12K.S11.N.A.LE.F4.T.Z.XDC.T.S.V.N.T). To conclude, the time series on loans granted by investment funds and the aggregate size of the bonds issued by non-financial corporations that are held/issued by each country (retrieved from the Securities Holdings Statistics by Sector dataset) are confidential series and cannot be shared.