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 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.

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 Jun 2025 about peak, trough, recession indicators, and USA.

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.

The base flow recession time constant (tau) is a hydrologic index that characterizes the ability of a ground-water system to supply flow to a stream draining from that system. Tau and other correlated hydrologic indices have been used as explanatory variables to greatly improve the predictive power of low-flow regression equations. Tau can also be used as an indicator of streamflow dependence on groundwater inflow to the channel. Tau values were calculated for 10 streamgages in the Niobrara National Scenic River study area. The calculated tau values were then used to create a kriged map. Kriging is a geostatistical method that can be used to determine optimal weights for measurements at sampled locations (streamgages) for the estimation of values at unsampled locations (ungaged sites). The kriged tau map could be used (1) as the basis for identifying areas with different hydrologic responsiveness, with differing potential to demonstrate the effects of management changes and (2) in the development of regional low-flow regression equations. The Geostatistical Analyst tools in ArcGIS Pro version 2.5.2 (Environmental Systems Research Institute, 2012) were used to create the kriged tau map and perform cross validation to determine the root mean square error (RMSE) of the tau map.

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.

This dataset is about books. It has 1 row and is filtered where the book is How to get a job in a recession : a comprehensive guide to job hunting in the 21st century, complete with masses of free downloadable bonuses. It features 7 columns including author, publication date, language, and book publisher.

Giuliano and Spilimbergo (2014) show that individuals who experienced a recession when young are more likely to favor redistribution in the short and long run. We revisit their analysis in three ways. First, we conduct a narrow replication in the General Social Survey and the World Values Survey; we successfully replicate the original results for outcomes that directly measure preferences for redistribution, but the results for other outcomes are less clear-cut. Second, adding recent survey waves yields results similar to the narrow replication. Third, a wide replication in a different dataset (International Social Survey Programme) corroborates the original results.

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.

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.

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.

The PWSD is a dataset that can be used to answer questions about various public workforce system programs and how these programs fit in with the overall public workforce system and the economy. It was designed primarily to be used as a tool to understand what has been occurring in the Wagner-Peyser program and contains data from quarter 1 of 1995 through quarter 4 of 2008. Also, it was designed to understand the relationship and flow of participants as they go through the public workforce system. The PWSD can be used to analyze these programs both individually and in combination. The PWSD contains economic variables, Unemployment Insurance System data, and data on programs funded by the Workforce Investment Act and Employment Service. Economic variables included are labor force, employment, unemployment, unemployment rate, and gross domestic product data.

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

The Great Recession undoubtedly reduced the electoral prospects of incumbent parties, coherently with the expectations of the economic vote theory. Yet, the exceptionality of the period may have displaced other elements of that theory, such as, for instance, the moderating impact that globalization is supposed to have on the retrospective mechanism. By using an original dataset comparing 168 elections in 38 democratic countries in the period 2000–2015, we detail how the crisis modified and even reversed that conditional effect. Furthermore, we differentiate our results by separating the impact of economic openness from that of political globalization. In so doing, we improve our understanding of the mechanisms that trigger the conditional effect on the economic vote in normal and exceptional times.

Hope Waddell Old Students’ Association (HWOSA) Annual Conference, Uyo

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

The Gross Domestic Product (GDP) in Hong Kong expanded 1.90 percent in the first quarter of 2025 over the previous quarter. This dataset provides - Hong Kong GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The Gross Domestic Product (GDP) in Italy expanded 0.70 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - Italy GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.