United States Recession Probability data was reported at 14.120 % in Oct 2019. This records a decrease from the previous number of 14.505 % for Sep 2019. United States Recession Probability data is updated monthly, averaging 7.668 % from Jan 1960 (Median) to Oct 2019, with 718 observations. The data reached an all-time high of 95.405 % in Dec 1981 and a record low of 0.080 % in Sep 1983. United States Recession Probability data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

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.

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.

United States FRB Recession Risk data was reported at 0.178 % in Apr 2025. This records a decrease from the previous number of 0.192 % for Mar 2025. United States FRB Recession Risk data is updated monthly, averaging 0.193 % from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 1.000 % in Oct 2008 and a record low of 0.022 % in Jul 2003. United States FRB Recession Risk data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

The excess bond premium (EBP) is a measure of investor sentiment or risk appetite in the corporate bond market. A credit spread index can be decomposed into two components: a component that captures the systematic movements in default risk of individual firms and a residual component: the excess bond premium that represents variation in the average price of bearing exposure to US corporate credit risk, above and beyond the compensation for expected defaults. The EBP component of corporate bond credit spreads that is not directly attributable to expected default risk provides an effective measure of investor sentiment or risk appetite in the corporate bond market.

Graph and download economic data for NBER based Recession Indicators for the United States from the Period following the Peak through the Trough (USREC) from Dec 1854 to May 2025 about peak, trough, recession indicators, and USA.

United States NBER: Recorded Recession data was reported at 0.000 Unit in Oct 2018. This stayed constant from the previous number of 0.000 Unit for Sep 2018. United States NBER: Recorded Recession data is updated monthly, averaging 0.000 Unit from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 1.000 Unit in Jun 2009 and a record low of 0.000 Unit in Oct 2018. United States NBER: Recorded Recession data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability. An interpretation of US Business Cycle Expansions and Contractions data provided by The National Bureau of Economic Research (NBER). A value of 1 is a recessionary period, while a value of 0 is an expansionary period.

United States Recession Prob: Yield Curve: 10 Year Treasury Yield data was reported at 3.150 % in Oct 2018. This records an increase from the previous number of 3.000 % for Sep 2018. United States Recession Prob: Yield Curve: 10 Year Treasury Yield data is updated monthly, averaging 5.750 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 15.320 % in Sep 1981 and a record low of 1.500 % in Jul 2016. United States Recession Prob: Yield Curve: 10 Year Treasury Yield data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.

During the Great Recession many incumbent parties were not confirmed in power by the ballots. The harsh law of the economic vote severely undermined their electoral chances. Yet it is unclear if they were punished by the absolute poor state of affairs, or by the relative deterioration of the economy; by a direct judgement of the domestic situation, or by its comparison with some external benchmark capturing more global dynamics; and whether or not the global crisis modified all these parameters. This exploratory analysis looks into all these issues using a dataset covering all the elections that took place in 38 democracies in the period 2000-2015, and contributing to the recent debate about the actual benchmarking of the state of the economy from behalf of voters. The Great Recession confirms its exceptional character, revealing that absolute reference points became more important than tailored benchmarks and short-term comparisons.

United States Recession Prob: Yield Curve: 3 Month Treasury Yield data was reported at 2.250 % in Oct 2018. This records an increase from the previous number of 2.130 % for Sep 2018. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data is updated monthly, averaging 4.620 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 16.300 % in May 1981 and a record low of 0.010 % in Dec 2011. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

United States FRB Recession Risk: Excess Bond Premium data was reported at -0.105 Basis Point in Apr 2025. This records a decrease from the previous number of -0.060 Basis Point for Mar 2025. United States FRB Recession Risk: Excess Bond Premium data is updated monthly, averaging -0.056 Basis Point from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 3.539 Basis Point in Oct 2008 and a record low of -1.026 Basis Point in Jul 2003. United States FRB Recession Risk: Excess Bond Premium data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

United States Recession Prob: Yield Curve: Spread data was reported at 0.856 % in Oct 2018. This records an increase from the previous number of 0.829 % for Sep 2018. United States Recession Prob: Yield Curve: Spread data is updated monthly, averaging 1.413 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 4.146 % in Sep 1982 and a record low of -3.505 % in Dec 1980. United States Recession Prob: Yield Curve: Spread data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

Graph and download economic data for OECD based Recession Indicators for India from the Period following the Peak through the Trough (DISCONTINUED) (INDREC) from May 1996 to Sep 2022 about peak, trough, recession indicators, and India.

We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models' parameters on these distributions. The small-sample performance is investigated, in terms of small numbers of forecasts and model estimation sample sizes. We show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey-based probability forecasts.

Recession Experience by Country.

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.

LON:ETX Stock: Are We Headed for a Recession?

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

Consumer Confidence in the United States increased to 60.70 points in June from 52.20 points in May of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Predicted probabilities of Self-Rated health by recession experiences.

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.

DTRTU Stock: Are We Headed for a Recession?

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