By November 2025, it is projected that there is a probability of 33.56 percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

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.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

Probability of Recession: United States data was reported at 0.995 % in Mar 2025. This records a decrease from the previous number of 1.031 % for Feb 2025. Probability of Recession: United States data is updated monthly, averaging 1.564 % from Jan 1980 (Median) to Mar 2025, with 543 observations. The data reached an all-time high of 87.972 % in May 2020 and a record low of 0.021 % in Jan 1980. Probability of Recession: United States data remains active status in CEIC and is reported by CEIC Data. The data is categorized under World Trend Plus’s CEIC Leading Indicator – Table US.S002: Probability of Recession.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to May 2025 about recession indicators, academic data, and USA.

We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.

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.

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MSVAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. The results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.

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.

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.

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.

This data package includes the underlying data files to replicate the data and charts presented in Egypt’s 2023-24 economic crisis: Will this time be different? by Ruchir Agarwal and Adnan Mazarei, PIIE Policy Brief 24-6.

If you use the data, please cite as: Agarwal, Ruchir, and Adnan Mazarei. 2024. Egypt’s 2023-24 economic crisis: Will this time be different?. PIIE Policy Brief 24-6. Washington, DC: Peterson Institute for International Economics.

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.

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.

IE:TSX 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

In March, 2020, a survey of Australians during the novel coronavirus pandemic revealed that 78 percent of respondents believed that the economy would get worse in the next month. Only slightly fewer respondents believed the economy would decline in three months time and just under 50 percent believed it would not improve in one year's time, compared to 32 percent who expected it to get better.

This metadata record describes observed and predicted baseflow recession characteristics for 300 streamflow gauges in the western United States and 282 streamflow gauges in the eastern United States. Specifically, this record describes (1) the streamflow gauge locations (west or east) in the United States (Location), (2) the U.S. Geological Survey streamflow gauge identification numbers (USGS_Site_Identifier), (3) observed regions of similar aquifer hydraulic properties (7 regions coded by color: blue, green, red, purple, grey, pink, and orange) by k-means clustering method (Observed_Class(k-means)), (4) predicted regions of similar aquifer hydraulic properties by random forest classification models (Predicted_Class(k-means)), (5) calculated long-term baseflow recession constant at streamflow gauges (Observed_a-long[ft^(-3/2)s^(-1/2)]), (6) predicted long-term baseflow recession constant by novel empirical and physical approach (Predicted_a-long(Novel)[ft^(-3/2)s^(-1/2)]), (7) pre ...

How do crises affect trade policy? We reconcile starkly diverging accounts in the literature by showing that economic adversity generates endogenous incentives not only for protection, but also for liberalization. We first develop formally the mechanisms by which two features of shocks---intensity and duration---influence the resources and political strategies of distressed firms. Our central insight is that policy adjustments to resuscitate afflicted industries typically generate "knock-on" effects on the profitability and political maneuverings of other firms in the economy. We incorporate these countervailing pressures in our analysis of trade policy competition. In the wake of crises, protection initially increases when impacted firms lobby for assistance, but then decreases as industries run low on resources to expend on lobbying and as firms in other industries mobilize to counter-lobby. We test our theoretical predictions using sub-national and cross-national data and present real-world illustrations to highlight the mechanisms driving our results.

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.

Crisis Management Service Market Outlook

The global crisis management service market size is projected to grow from USD 10.5 billion in 2023 to USD 23.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 9.5% during the forecast period. This growth is driven by the increasing frequency of natural and man-made disasters, which necessitates robust crisis management solutions across various sectors. The heightened awareness among organizations to prepare for unforeseen events, coupled with regulatory requirements, is also propelling market expansion.

One of the primary growth factors for the crisis management service market is the growing incidence of natural disasters, cyber-attacks, and geopolitical tensions. Organizations across the globe are recognizing the importance of being prepared for these events to minimize their impact on operations and ensure business continuity. The rising occurrence of such crises has led governments and private entities to invest heavily in crisis management services, including consulting, training, and support and maintenance. Additionally, the increasing complexity of crises, often involving multiple stakeholders and requiring coordinated responses, is further driving the demand for these services.

Another significant growth factor is the advancement in technology, which has revolutionized crisis management services. The integration of advanced analytics, artificial intelligence, and machine learning in crisis management solutions has enhanced the ability to predict, prepare for, and respond to crises more effectively. These technologies enable organizations to analyze vast amounts of data, identify potential risks, and develop proactive strategies. The adoption of cloud-based solutions also facilitates real-time communication and coordination during crises, making the response more efficient and effective.

The rising regulatory requirements and standards related to crisis management are also contributing to the market's growth. Governments and regulatory bodies worldwide are mandating organizations to implement robust crisis management plans and conduct regular training and drills. Compliance with these regulations not only helps organizations to mitigate the impact of crises but also protects them from legal and financial repercussions. The increasing focus on risk management and corporate governance is further encouraging organizations to invest in comprehensive crisis management services.

In the realm of crisis management, Incident Response Service plays a pivotal role in ensuring organizations can swiftly address and mitigate the impacts of unexpected events. This service is designed to provide immediate support and expertise when a crisis occurs, enabling businesses to respond effectively and minimize potential damages. Incident Response Service encompasses a range of activities, including the identification of the crisis source, containment of the situation, and recovery strategies to restore normal operations. By having a dedicated team or service in place, organizations can significantly reduce downtime and protect their reputation during critical incidents. The integration of Incident Response Service into a broader crisis management strategy ensures that businesses are not only prepared for crises but also equipped to handle them efficiently when they arise.

Regionally, North America is expected to dominate the crisis management service market due to the high prevalence of natural disasters and cyber threats in the region. The presence of major market players and the early adoption of advanced technologies are other factors contributing to the region's market leadership. Europe is also anticipated to witness significant growth, driven by stringent regulatory requirements and a strong focus on risk management. The Asia Pacific region is expected to grow at the highest CAGR, fueled by rapid economic development, increasing disaster occurrences, and growing awareness about crisis preparedness.

Service Type Analysis

The crisis management service market is segmented by service type into consulting, training, and support and maintenance. Consulting services are critical as they help organizations understand their vulnerabilities and develop comprehensive crisis management plans. These services include risk assessment, crisis communication planning, and business continuity planning. The demand for consulting services is expected to grow significantly as organizations strive to im