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 Q1 2025 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.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in A program for strengthening the Federal Reserve’s ability to fight the next recession, PIIE Working Paper 20-5.

If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2020). A program for strengthening the Federal Reserve’s ability to fight the next recession. PIIE Working Paper 20-5. Peterson Institute for International Economics.

This dataset is about books. It has 1 row and is filtered where the book is Education in recession : crisis in county hall and classroom. It features 7 columns including author, publication date, language, and book publisher.

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.

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/

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.

This dataset is about books. It has 1 row and is filtered where the book is Corporate dreams : big business in American democracy from the Great Depression to the great recession. It features 7 columns including author, publication date, language, and book publisher.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Are Central Banks Out of Ammunition to Fight a Recession? Not Quite, PIIE Policy Brief 19-18.

If you use the data, please cite as: Gagnon, Joseph E., and Christopher G. Collins. (2019). Are Central Banks Out of Ammunition to Fight a Recession? Not Quite. PIIE Policy Brief 19-18. Peterson Institute for International Economics.

Context

This dataset was built using the Philadelphia Federal Reserve's State Coincident Indices and the Bry-Boschan Method for business cycle dating. In the tradition of Owyang, Piger, et al. business cycles are calculated on the state level which provides interesting analysis opportunities for looking at recession timing for different regions or sectors present in different states. The MSA level data utilizes the Economic Coincident Indices available on the St. Louis FRED website and uses a variant of the non-parametric algorithm described in Metro Business Cycles (Arias et al. 2016) to date MSA level recessions.

Content

This data is from 1982 through 2018 and includes whether the economy is in a recession or not, with forward looking and backward looking data available for observations as well. Additionally, various FRED St. Louis series were joined, like the University of Michigan Consumer Sentiment Index and the Global Price of Brent Crude. The 2012 value added as a percent for different NAICS groups is included as well for sectoral analysis, although better data over time for this would prove beneficial. The industries file attempts to correct this, but has fewer years available.

Acknowledgements

Special thanks to the researchers at the Federal Reserve Banks of Philadelphia and St. Louis for collecting and making available much of the data that went into this dataset.

Inspiration

I was inspired by researchers that have attempted to take business cycle dating to the state and MSA level. Local business cycle dating methodologies allow for a more robust understanding of what goes into a recession and how sectoral composition can affect a state or MSA's "resilience" to recessions. This could have applications for weighting business cycle risk for companies based on geographic dispersion of customers, as well as local policymakers if local forecasting could be done successfully.

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

During the Great Recession, exceptionally harsh economic conditions were often countered by austerity policies that, according to many, further worsened and protracted the negative conjuncture. Both elements, the poor state of the economy and the contractionary manoeuvers, are supposed to reduce the electoral prospects for incumbents. In this article, we compare the relative explanatory powers of these two theories before and during the economic crisis. We demonstrate that in normal times citizens are fiscally responsible, whereas during the Great Recession, and under certain conditions, austerity policies systematically reduced the support for incumbents on top of the state of the economy. This happened when the burdens of the manoeuvers were shared by many, in more equal societies, when the country was constrained by external conditionalities and when readjustments were mostly based on tax increases.

The Gross Domestic Product (GDP) in Germany contracted 0.10 percent in the second 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.

This new time series dataset on global streamflow indices is calculated from daily streamflow records after data quality control and includes 79 indices over seven components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 5548 river reaches globally. The indices time series in the dataset are available until 2021, the lengths of which vary from 30 to 215 years with an average of around 66 years. Restricted-access streamflow data of typical river basins in China are included in the dataset. Compared to existing global datasets, this global dataset covers more indices, especially indices characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Other Collections. The data include parameters of others with a geographic location of Greenland. The time period coverage is from 25280 to 210 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The Phase I project will develop a suite of diagnostic sensors using Direct Write technology to measure temperature, surface recession depth, and heat flux of an ablative thermal protection system (TPS) in real time, which can be integrated to support TPS evaluation and in-situ diagnostics during planetary entry. Standalone heat flux sensors and those fabricated by direct deposition will be developed and demonstrated for integration within TPS materials for use in extreme re-entry conditions. The intent is to use the sensors for real time heat flux measurements to validate new materials and systems, as well as for flight structures where space and accessibility are limited. Methods for incorporating thermocouples, heat flux and recession sensors using Direct Write technology will be developed to provide accurate sensing capabilities. Notably, recession tolerant heat flux sensors will be designed and fabricated to demonstrate feasibility of this new heat flux sensor technology and subsequent instrumentation capability for TPS.

The Gross Domestic Product (GDP) in Italy expanded 0.40 percent in the second 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.

Sample data for HESS-2019-205 submission

Description: This file contains the event magnitudes and spacing for Cases 1 & 3 presented in the submitted manuscript to HESS titled "Recession analysis 42 years later - work yet to be done".

CVS File: This file is an ordered set of the normalized event magnitude [-] and the start date fo the event (Time/Timescale [-])

Matlab File: The file is presented is in a .mat file extension created in Matlab. The data is divided into 3 columns: mag, value, and start_locs. The column of "Mag" defines the event magnitudes, which are log-normally distributed with a mean 1 of a standard deviation of 1. The column of "value" defines the event duration which has a mean of 2.5 and a standard deviation of 1. The "start_locs" column as the cumulative event durations that identify the start time of each event. Below is the associated Matlab code used to create the file: %% Matlab Code %% mag= lognrnd(1,1[number_of_events,1]); %create log-normally distributed dataset of event magnitudes for a defined number of events mag(mag<0)=1; %remove any negative magnitudes value=round(lognrnd(2.5,1,[number_of_events,1])); %create log-normally distributed dataset of event durations for a defined number of events value(value<=0)=1; %remove any negative durations start_locs=[2;cumsum(value)]; %create cumulative event start time-series