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

Georgia GDP Index: GVA: Agriculture, Forestry and Fishing data was reported at 97.452 Same Qtr PY=100 in Jun 2018. This records an increase from the previous number of 96.243 Same Qtr PY=100 for Mar 2018. Georgia GDP Index: GVA: Agriculture, Forestry and Fishing data is updated quarterly, averaging 99.443 Same Qtr PY=100 from Mar 1998 (Median) to Jun 2018, with 82 observations. The data reached an all-time high of 120.741 Same Qtr PY=100 in Sep 2007 and a record low of 78.806 Same Qtr PY=100 in Sep 2006. Georgia GDP Index: GVA: Agriculture, Forestry and Fishing data remains active status in CEIC and is reported by National Statistics Office of Georgia. The data is categorized under Global Database’s Georgia – Table GE.A017: GDP: by Industry: Index: Same Quarter Previous Year=100.

In 2024, the finance, real estate, insurance, rental, and leasing industry added the most value to the GDP of the United States. In that year, this industry added 6.2 trillion U.S. dollars to the national GDP. Gross Domestic Product Gross domestic product is a measure of how much a country produces in a certain amount of time. Countries with a high GDP tend to have large economies, for example, the United States. However, GDP does not take into consideration the cost of living and inflation rates, so it is not a good measure of the standard of living. GDP per capita at purchasing power parity is thought to be more reflective of living conditions within a particular country. U.S. GDP California added the largest amount of value to the real GDP of the U.S. in 2022. California was followed by Texas and New York. In California, the professional and business services industry was the most valuable to GDP in 2022. In New York, the finance, insurance, real estate, rental, and leasing industry added the most value to the state GDP. While the business sector added the highest value to the U.S. real GDP in 2021, it was the information industry that had the biggest percentage change in value added to the GDP between 2010 and 2021.

In 2024, the growth of the real gross domestic product (GDP) in Argentina amounted to about -1.72 percent. Between 1980 and 2024, the figure dropped by approximately 2.42 percentage points, though the decline followed an uneven course rather than a steady trajectory. From 2024 to 2030, the growth will rise by around 4.69 percentage points, showing an overall upward trend with periodic ups and downs.This indicator describes the annual change in the gross domestic product at constant prices, expressed in national currency units. Here the gross domestic product represents the total value of the final goods and services produced during a year.

Kazakhstan GDP per Capita: Year to Date data was reported at 1,364.700 KZT th in Jun 2018. This records an increase from the previous number of 648.100 KZT th for Mar 2018. Kazakhstan GDP per Capita: Year to Date data is updated quarterly, averaging 912.600 KZT th from Dec 2007 (Median) to Jun 2018, with 43 observations. The data reached an all-time high of 2,943.900 KZT th in Dec 2017 and a record low of 193.000 KZT th in Mar 2009. Kazakhstan GDP per Capita: Year to Date data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.A015: GDP: per Capita: Current Price: ytd: by Region.

GDP Index: Guangxi data was reported at 104.200 Prev Year=100 in 2024. This records an increase from the previous number of 104.100 Prev Year=100 for 2023. GDP Index: Guangxi data is updated yearly, averaging 108.345 Prev Year=100 from Dec 1952 (Median) to 2024, with 73 observations. The data reached an all-time high of 126.600 Prev Year=100 in 1969 and a record low of 85.700 Prev Year=100 in 1961. GDP Index: Guangxi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under Global Database’s China – Table CN.AB: Gross Domestic Product: Index: by Province.

【リソース】Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Nominal Gross Domestic Product (original series) (csv:28KB) / GDP (expenditure approach) and its components / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annual Nominal GDP (fiscal year) (csv:8KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Nominal Gross Domestic Product (seasonally adjusted series) (csv:29KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annual Nominal GDP (calendar year) (csv:8KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Real Gross Domestic Product (original series) (csv:31KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annual Real GDP (fiscal year) (csv:9KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Real Gross Domestic Product (seasonally adjusted series) (csv:32KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annual Real GDP (calendar year) (csv:9KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at current prices: original series) (csv:14KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at current prices: fiscal year) (csv:5KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous quarter (at current prices: seasonally adjusted series) (csv:14KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at current prices: calendar year) (csv:4KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at chained (2011) prices: original series) (csv:15KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at chained (2011) prices: fiscal year) (csv:5KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous quarter (at chained (2011) prices: seasonally adjusted series) (csv:15KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Changes from the previous year (at chained (2011) prices: calendar year) (csv:4KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annualized rate of changes from the previous quarter (at current prices: seasonally adjusted series) (csv:13KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Annualized rate of changes from the previous quarter (at chained (2011) prices: seasonally adjusted series) (csv:13KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Nominal GDP (original series) (csv:15KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Annual Nominal GDP (fiscal year) (csv:5KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Nominal GDP (seasonally adjusted series) (csv:15KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Annual Nominal GDP (calendar year) (csv:5KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Real GDP (original series) (csv:15KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Annual Real GDP (fiscal year) (csv:6KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Real GDP (seasonally adjusted series) (csv:16KB) / Quarterly Estimates of GDP Apr.-Jun. 2018 (The 2nd Preliminary) : GDP (expenditure approach) and its components : Contributions to Changes in Annual Real GDP (calendar Year) (csv:5KB) / Quarterly Estimates of GDP

Vietnam GDP: HCMC: Year to Date: IC: Industry data was reported at 170,590.000 VND bn in Sep 2018. This records an increase from the previous number of 107,991.000 VND bn for Jun 2018. Vietnam GDP: HCMC: Year to Date: IC: Industry data is updated quarterly, averaging 92,268.500 VND bn from Jun 2004 (Median) to Sep 2018, with 54 observations. The data reached an all-time high of 1,193,626.000 VND bn in Jun 2016 and a record low of 18,056.000 VND bn in Mar 2007. Vietnam GDP: HCMC: Year to Date: IC: Industry data remains active status in CEIC and is reported by Ho Chi Minh City Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.A029: Gross Domestic Product: Ho Chi Minh City: Quarterly.

The Gross Domestic Product (GDP) in Slovenia contracted 0.80 percent in the first quarter of 2025 over the previous quarter. This dataset provides - Slovenia GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Infra-Annual Labor Statistics: Unemployment Rate Total: From 15 to 24 Years for Mexico (LRUN24TTMXQ156S) from Q1 2005 to Q1 2025 about 15 to 24 years, Mexico, unemployment, and rate.

Germany’s GDP per capita stood at almost 54,989.76 U.S. dollars in 2024. Germany ranked among the top 20 countries worldwide with the highest GDP per capita in 2021 – Luxembourg, Ireland and Switzerland were ranked the top three nations. Rising annual income in Germany The average annual wage in Germany has increased by around 5,000 euros since 2000, reaching in excess of 39,000 euros in 2016. Germany had the tenth-highest average annual wage among selected European Union countries in 2017, ranking between France and the United Kingdom. Growing employment More than two thirds of the working population in Germany are employed in the service sector, which generated the greatest share of the country’s GDP in 2018. Unemployment in Germany soared to its highest level in decades in 2005, but the rate has since dropped to below 3.5 percent. The youth unemployment rate in Germany has more than halved since 2005 and currently stands around 6.5 percent.

The Gross Domestic Product per capita in Georgia was last recorded at 6840.01 US dollars in 2024. The GDP per Capita in Georgia is equivalent to 54 percent of the world's average. This dataset provides - Georgia GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP: Year to Date: Secondary Industry: Zhejiang: Wenzhou data was reported at 356.800 RMB bn in Dec 2024. This records an increase from the previous number of 280.120 RMB bn for Sep 2024. GDP: Year to Date: Secondary Industry: Zhejiang: Wenzhou data is updated quarterly, averaging 82.800 RMB bn from Dec 1978 (Median) to Dec 2024, with 90 observations. The data reached an all-time high of 360.670 RMB bn in Dec 2023 and a record low of 0.474 RMB bn in Dec 1978. GDP: Year to Date: Secondary Industry: Zhejiang: Wenzhou data remains active status in CEIC and is reported by Wenzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Secondary Industry: Quarterly.

CN:(GDP) Gross Domestic ProductIndex: YoY: Year to Date: Jiangxi: Xinyu data was reported at 100.600 Prev Year=100 in Dec 2024. This records an increase from the previous number of 98.900 Prev Year=100 for Sep 2024. CN:(GDP) Gross Domestic ProductIndex: YoY: Year to Date: Jiangxi: Xinyu data is updated quarterly, averaging 108.400 Prev Year=100 from Jun 2010 (Median) to Dec 2024, with 59 observations. The data reached an all-time high of 117.800 Prev Year=100 in Mar 2021 and a record low of 93.100 Prev Year=100 in Mar 2020. CN:(GDP) Gross Domestic ProductIndex: YoY: Year to Date: Jiangxi: Xinyu data remains active status in CEIC and is reported by Xinyu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Index: Quarterly.

GDP Index: Secondary Industry: Industry: Shandong data was reported at 108.600 Prev Year=100 in 2021. This records an increase from the previous number of 103.600 Prev Year=100 for 2020. GDP Index: Secondary Industry: Industry: Shandong data is updated yearly, averaging 112.150 Prev Year=100 from Dec 1950 (Median) to 2021, with 72 observations. The data reached an all-time high of 158.100 Prev Year=100 in 1952 and a record low of 64.800 Prev Year=100 in 1961. GDP Index: Secondary Industry: Industry: Shandong data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AB: Gross Domestic Product: Index: by Province.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Graph and download economic data for Infra-Annual Labor Statistics: Unemployment Rate Total: From 15 to 64 Years for Poland (LRUN64TTPLQ156N) from Q1 1999 to Q4 2024 about Poland, 15 to 64 years, unemployment, and rate.

Graph and download economic data for Infra-Annual Labor Statistics: Unemployment Rate Total: From 55 to 64 Years for Finland (LRUN55TTFIQ156S) from Q1 1998 to Q4 2024 about 55 to 64 years, Finland, unemployment, and rate.

Graph and download economic data for Infra-Annual Labor Statistics: Monthly Unemployment Rate Female: From 15 to 24 Years for Japan (LRHU24FEJPQ156S) from Q1 1970 to Q1 2025 about 15 to 24 years, females, harmonized, Japan, unemployment, and rate.

Graph and download economic data for Infra-Annual Labor Statistics: Unemployment Female: From 15 to 64 Years for United States (LFUN64FEUSM647S) from Jan 1970 to May 2025 about 15 to 64 years, females, unemployment, and USA.