Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment Rate in China remained unchanged at 5 percent in June. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in Germany remained unchanged at 6.30 percent in July. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Unemployment Rate in Brazil decreased to 5.80 percent in June from 6.20 percent in May of 2025. This dataset provides the latest reported value for - Brazil Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is the relationship between unemployment and inflation?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Ireland IE: NAIRU: Equilibrium Unemployment Rate data was reported at 7.152 % in 2022. This records an increase from the previous number of 7.136 % for 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 8.407 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 12.280 % in 1990 and a record low of 7.136 % in 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Canada CA: NAIRU: Unemployment Gap data was reported at -0.577 % in 2022. This records an increase from the previous number of -1.614 % for 2021. Canada CA: NAIRU: Unemployment Gap data is updated yearly, averaging -0.461 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 0.643 % in 2007 and a record low of -3.360 % in 1993. Canada CA: NAIRU: Unemployment Gap data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. GAPUNR - Unemployment gap Difference of nairu and unemployment rate OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

Norway NO: NAIRU: Equilibrium Unemployment Rate data was reported at 3.514 % in 2022. This records an increase from the previous number of 3.480 % for 2021. Norway NO: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 3.462 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 4.744 % in 1993 and a record low of 2.775 % in 1985. Norway NO: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Unemployment Rate in Greece decreased to 7.90 percent in May from 8.30 percent in April of 2025. This dataset provides the latest reported value for - Greece Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Lithuania LT: NAIRU: Equilibrium Unemployment Rate data was reported at 6.680 % in 2022. This records a decrease from the previous number of 6.683 % for 2021. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 7.322 % from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 9.571 % in 2011 and a record low of 5.965 % in 2002. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is There a Netflix Effect on Unemployment? A Statistical Exploration

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Czech Republic CZ: NAIRU: Unemployment Gap data was reported at 0.439 % in 2022. This records an increase from the previous number of 0.364 % for 2021. Czech Republic CZ: NAIRU: Unemployment Gap data is updated yearly, averaging 0.006 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 2.532 % in 1996 and a record low of -1.243 % in 2000. Czech Republic CZ: NAIRU: Unemployment Gap data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. GAPUNR - Unemployment gap Difference of nairu and unemployment rate OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Norway NO: NAIRU: Unemployment Gap data was reported at -0.489 % in 2022. This records an increase from the previous number of -1.206 % for 2021. Norway NO: NAIRU: Unemployment Gap data is updated yearly, averaging -0.307 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 1.001 % in 1987 and a record low of -1.206 % in 2021. Norway NO: NAIRU: Unemployment Gap data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. GAPUNR - Unemployment gap Difference of nairu and unemployment rate OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data was reported at 3.891 % in 2022. This records an increase from the previous number of 3.831 % for 2021. Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 6.107 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 7.608 % in 1999 and a record low of 3.831 % in 2020. Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Israel IL: NAIRU: Unemployment Gap data was reported at -0.815 % in 2022. This records an increase from the previous number of -1.343 % for 2021. Israel IL: NAIRU: Unemployment Gap data is updated yearly, averaging -0.153 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 1.692 % in 2008 and a record low of -1.831 % in 2003. Israel IL: NAIRU: Unemployment Gap data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. GAPUNR - Unemployment gap Difference of nairu and unemployment rate OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Canada CA: NAIRU: Equilibrium Unemployment Rate data was reported at 6.243 % in 2022. This records a decrease from the previous number of 6.251 % for 2021. Canada CA: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 7.065 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 8.006 % in 1993 and a record low of 6.243 % in 2022. Canada CA: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

This paper provides an empirical investigation of the wage, price and unemployment dynamics that have taken place in Spain during the last two decades. The aim of this paper is to shed light on the impact of the European economic integration on Spanish labour market and the convergence to a European level of prosperity. We found that the Balassa-Samuelson effect, product market competition, and capital liberalization have been the main driving forces in this period. The adjustment dynamics show that Spanish inflation has adjusted in the long run to the European purchasi ng power parity level (as measured by the German price level) corrected for the Balassa-Samuelson effect. In the medium run this long-run convergence was achieved by two types of Phillips curve mechanisms; one where the inflation/unemployment trade-off was triggered off for different levels of the interest rate and real wage costs, another one where the trade-off was a function of the real exchange rate and the interest rate. Excess wages and/or increasing cost levels in the tradable secto r led to higher unemployment rather than higher prices. Thus, much of the burden of adjustment was carried by unemployment in this period.