56 datasets found
  1. US Recession Dataset

    • kaggle.com
    zip
    Updated May 14, 2023
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    Shubhaansh Kumar (2023). US Recession Dataset [Dataset]. https://www.kaggle.com/datasets/shubhaanshkumar/us-recession-dataset
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    zip(39062 bytes)Available download formats
    Dataset updated
    May 14, 2023
    Authors
    Shubhaansh Kumar
    License

    https://cdla.io/sharing-1-0/https://cdla.io/sharing-1-0/

    Area covered
    United States
    Description

    This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

    There are 20 columns and 343 rows spanning 1990-04 to 2022-10

    The columns are:

    1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

    2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

    3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

    4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

    5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

    6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

    7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

    8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

    9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

  2. Great Recession: unemployment rate in the G7 countries 2007-2011

    • statista.com
    Updated Nov 23, 2022
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    Statista (2022). Great Recession: unemployment rate in the G7 countries 2007-2011 [Dataset]. https://www.statista.com/statistics/1346779/unemployment-rate-g7-great-recession/
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    Dataset updated
    Nov 23, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2007 - 2011
    Area covered
    Worldwide
    Description

    With the collapse of the U.S. housing market and the subsequent financial crisis on Wall Street in 2007 and 2008, economies across the globe began to enter into deep recessions. What had started out as a crisis centered on the United States quickly became global in nature, as it became apparent that not only had the economies of other advanced countries (grouped together as the G7) become intimately tied to the U.S. financial system, but that many of them had experienced housing and asset price bubbles similar to that in the U.S.. The United Kingdom had experienced a huge inflation of housing prices since the 1990s, while Eurozone members (such as Germany, France and Italy) had financial sectors which had become involved in reckless lending to economies on the periphery of the EU, such as Greece, Ireland and Portugal. Other countries, such as Japan, were hit heavily due their export-led growth models which suffered from the decline in international trade. Unemployment during the Great Recession As business and consumer confidence crashed, credit markets froze, and international trade contracted, the unemployment rate in the most advanced economies shot up. While four to five percent is generally considered to be a healthy unemployment rate, nearing full employment in the economy (when any remaining unemployment is not related to a lack of consumer demand), many of these countries experienced rates at least double that, with unemployment in the United States peaking at almost 10 percent in 2010. In large countries, unemployment rates of this level meant millions or tens of millions of people being out of work, which led to political pressures to stimulate economies and create jobs. By 2012, many of these countries were seeing declining unemployment rates, however, in France and Italy rates of joblessness continued to increase as the Euro crisis took hold. These countries suffered from having a monetary policy which was too tight for their economies (due to the ECB controlling interest rates) and fiscal policy which was constrained by EU debt rules. Left with the option of deregulating their labor markets and pursuing austerity policies, their unemployment rates remained over 10 percent well into the 2010s. Differences in labor markets The differences in unemployment rates at the peak of the crisis (2009-2010) reflect not only the differences in how economies were affected by the downturn, but also the differing labor market institutions and programs in the various countries. Countries with more 'liberalized' labor markets, such as the United States and United Kingdom experienced sharp jumps in their unemployment rate due to the ease at which employers can lay off workers in these countries. When the crisis subsided in these countries, however, their unemployment rates quickly began to drop below those of the other countries, due to their more dynamic labor markets which make it easier to hire workers when the economy is doing well. On the other hand, countries with more 'coordinated' labor market institutions, such as Germany and Japan, experiences lower rates of unemployment during the crisis, as programs such as short-time work, job sharing, and wage restraint agreements were used to keep workers in their jobs. While these countries are less likely to experience spikes in unemployment during crises, the highly regulated nature of their labor markets mean that they are slower to add jobs during periods of economic prosperity.

  3. Great Recession: delinquency rate by loan type in the U.S. 2007-2010

    • statista.com
    Updated Oct 28, 2022
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    Statista (2022). Great Recession: delinquency rate by loan type in the U.S. 2007-2010 [Dataset]. https://www.statista.com/statistics/1342448/global-financial-crisis-us-economic-indicators/
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    Dataset updated
    Oct 28, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2007 - 2012
    Area covered
    United States
    Description

    The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

    Subprime and the collapse of the U.S. mortgage market

    The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

    Market Panic and The Great Recession

    As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

  4. F

    Real-time Sahm Rule Recession Indicator

    • fred.stlouisfed.org
    json
    Updated Nov 20, 2025
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    (2025). Real-time Sahm Rule Recession Indicator [Dataset]. https://fred.stlouisfed.org/series/SAHMREALTIME
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    jsonAvailable download formats
    Dataset updated
    Nov 20, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

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

  5. Annual Fed funds effective rate in the U.S. 1990-2024

    • statista.com
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    Statista, Annual Fed funds effective rate in the U.S. 1990-2024 [Dataset]. https://www.statista.com/statistics/247941/federal-funds-rate-level-in-the-united-states/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The U.S. federal funds rate peaked in 2023 at its highest level since the 2007-08 financial crisis, reaching 5.33 percent by December 2023. A significant shift in monetary policy occurred in the second half of 2024, with the Federal Reserve implementing regular rate cuts. By December 2024, the rate had declined to 4.48 percent. What is a central bank rate? The federal funds rate determines the cost of overnight borrowing between banks, allowing them to maintain necessary cash reserves and ensure financial system liquidity. When this rate rises, banks become more inclined to hold rather than lend money, reducing the money supply. While this decreased lending slows economic activity, it helps control inflation by limiting the circulation of money in the economy. Historic perspective The federal funds rate historically follows cyclical patterns, falling during recessions and gradually rising during economic recoveries. Some central banks, notably the European Central Bank, went beyond traditional monetary policy by implementing both aggressive asset purchases and negative interest rates.

  6. Data from: Recession Probabilities

    • clevelandfed.org
    Updated Aug 23, 2016
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    Federal Reserve Bank of Cleveland (2016). Recession Probabilities [Dataset]. https://www.clevelandfed.org/publications/economic-commentary/2016/ec-201609-recession-probabilities
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    Dataset updated
    Aug 23, 2016
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    Description

    Statistical models that estimate 12-month-ahead recession probabilities using the term spread have been around for many years. However, the reliability of the term spread as a predictor may have been affected by short-term interest rates being at zero. At the zero lower bound, long-term yields cannot go too far into negative territory due to the portfolio constraints of institutional investors. Therefore, the yield curve may not invert when it should or as much as it should despite the anticipated path of the economy. I enhance the simple model with two variables that should have predictive power for recessions.

  7. c

    Rising Interest Rate Risk at U.S. Banks

    • clevelandfed.org
    Updated Jun 24, 2014
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    Federal Reserve Bank of Cleveland (2014). Rising Interest Rate Risk at U.S. Banks [Dataset]. https://www.clevelandfed.org/publications/economic-commentary/2014/ec-201412-rising-interest-rate-risk-at-us-banks
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    Dataset updated
    Jun 24, 2014
    Dataset authored and provided by
    Federal Reserve Bank of Cleveland
    Area covered
    United States
    Description

    Average interest rate risk in the banking system has been increasing since the end of the financial crisis and is almost back to its pre-recession level. But the increase has not occurred uniformly at large and small banks. At big banks, risk, while increasing, hasn’t yet reached its pre-recession high. It’s in small banks where we see a steep rise in interest rate risk. The big banks’ exposure is being driven mainly by their liabilities. At small banks, it is coming from both their assets and liabilities.

  8. Replication data for: Understanding the Great Recession

    • openicpsr.org
    Updated Jan 1, 2015
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    Lawrence J. Christiano; Martin S. Eichenbaum; Mathias Trabandt (2015). Replication data for: Understanding the Great Recession [Dataset]. http://doi.org/10.3886/E114095V1
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    Dataset updated
    Jan 1, 2015
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Lawrence J. Christiano; Martin S. Eichenbaum; Mathias Trabandt
    Description

    We argue that the vast bulk of movements in aggregate real economic activity during the Great Recession were due to financial frictions. We reach this conclusion by looking through the lens of an estimated New Keynesian model in which firms face moderate degrees of price rigidities, no nominal rigidities in wages, and a binding zero lower bound constraint on the nominal interest rate. Our model does a good job of accounting for the joint behavior of labor and goods markets, as well as inflation, during the Great Recession. According to the model the observed fall in total factor productivity and the rise in the cost of working capital played critical roles in accounting for the small drop in inflation that occurred during the Great Recession. (JEL E12, E23, E24, E31, E32, E52)

  9. Volcker Shock: federal funds, unemployment and inflation rates 1979-1987

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Volcker Shock: federal funds, unemployment and inflation rates 1979-1987 [Dataset]. https://www.statista.com/statistics/1338105/volcker-shock-interest-rates-unemployment-inflation/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1979 - 1987
    Area covered
    United States
    Description

    The Volcker Shock was a period of historically high interest rates precipitated by Federal Reserve Chairperson Paul Volcker's decision to raise the central bank's key interest rate, the Fed funds effective rate, during the first three years of his term. Volcker was appointed chairperson of the Fed in August 1979 by President Jimmy Carter, as replacement for William Miller, who Carter had made his treasury secretary. Volcker was one of the most hawkish (supportive of tighter monetary policy to stem inflation) members of the Federal Reserve's committee, and quickly set about changing the course of monetary policy in the U.S. in order to quell inflation. The Volcker Shock is remembered for bringing an end to over a decade of high inflation in the United States, prompting a deep recession and high unemployment, and for spurring on debt defaults among developing countries in Latin America who had borrowed in U.S. dollars.

    Monetary tightening and the recessions of the early '80s

    Beginning in October 1979, Volcker's Fed tightened monetary policy by raising interest rates. This decision had the effect of depressing demand and slowing down the U.S. economy, as credit became more expensive for households and businesses. The Fed funds rate, the key overnight rate at which banks lend their excess reserves to each other, rose as high as 17.6 percent in early 1980. The rate was allowed to fall back below 10 percent following this first peak, however, due to worries that inflation was not falling fast enough, a second cycle of monetary tightening was embarked upon starting in August of 1980. The rate would reach its all-time peak in June of 1981, at 19.1 percent. The second recession sparked by these hikes was far deeper than the 1980 recession, with unemployment peaking at 10.8 percent in December 1980, the highest level since The Great Depression. This recession would drive inflation to a low point during Volcker's terms of 2.5 percent in August 1983.

    The legacy of the Volcker Shock

    By the end of Volcker's terms as Fed Chair, inflation was at a manageable rate of around four percent, while unemployment had fallen under six percent, as the economy grew and business confidence returned. While supporters of Volcker's actions point to these numbers as proof of the efficacy of his actions, critics have claimed that there were less harmful ways that inflation could have been brought under control. The recessions of the early 1980s are cited as accelerating deindustrialization in the U.S., as manufacturing jobs lost in 'rust belt' states such as Michigan, Ohio, and Pennsylvania never returned during the years of recovery. The Volcker Shock was also a driving factor behind the Latin American debt crises of the 1980s, as governments in the region defaulted on debts which they had incurred in U.S. dollars. Debates about the validity of using interest rate hikes to get inflation under control have recently re-emerged due to the inflationary pressures facing the U.S. following the Coronavirus pandemic and the Federal Reserve's subsequent decision to embark on a course of monetary tightening.

  10. FRED - Dataset USREC

    • kaggle.com
    zip
    Updated Nov 21, 2023
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    Felipe Teti (2023). FRED - Dataset USREC [Dataset]. https://www.kaggle.com/datasets/felipeteti/fred-dataset-usrec/data
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    zip(295567 bytes)Available download formats
    Dataset updated
    Nov 21, 2023
    Authors
    Felipe Teti
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Inspired by:

    Modeling and predicting U.S. recessions using machine learning techniques

    As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)

    Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)

    128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market

  11. Data from: Monetary Policy Tightening and Long-Term Interest Rates

    • clevelandfed.org
    Updated Sep 1, 2013
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    Federal Reserve Bank of Cleveland (2013). Monetary Policy Tightening and Long-Term Interest Rates [Dataset]. https://www.clevelandfed.org/publications/economic-commentary/2013/ec-201308-monetary-policy-tightening-and-long-term-interest-rates
    Explore at:
    Dataset updated
    Sep 1, 2013
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    Description

    The Federal Open Market Committee (FOMC) has maintained an accommodative monetary policy ever since the 2007 recession, and some financial market participants are concerned that long-term interest rates may increase more than should be expected when the Committee starts to tighten. But a look at five historical episodes of monetary policy tightening suggests that such an outcome is more likely when markets are surprised by policy actions or economic developments. Given the Fed’s new policy tools, especially its evolution toward more transparent communications, the odds of a surprise are far less likely now.

  12. TA:TSX Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Aug 22, 2023
    + more versions
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    KappaSignal (2023). TA:TSX Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/08/tatsx-stock-are-we-headed-for-recession.html
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    Dataset updated
    Aug 22, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    TA: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

  13. F

    Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates

    • fred.stlouisfed.org
    json
    Updated Oct 31, 2025
    + more versions
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    (2025). Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates [Dataset]. https://fred.stlouisfed.org/series/PRIME
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Oct 31, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates (PRIME) from 1955-08-04 to 2025-10-30 about prime, loans, interest rate, banks, depository institutions, interest, rate, and USA.

  14. o

    Data and Code for: Liquidity Traps A Unified Theory of the Great Depression...

    • openicpsr.org
    delimited
    Updated Aug 22, 2025
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    Gauti B. Eggertsson; Sergey K. Egiev (2025). Data and Code for: Liquidity Traps A Unified Theory of the Great Depression and the Great Recession [Dataset]. http://doi.org/10.3886/E237322V1
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    Aug 22, 2025
    Dataset provided by
    American Economic Association
    Authors
    Gauti B. Eggertsson; Sergey K. Egiev
    Time period covered
    Jan 1, 1929 - Dec 31, 1942
    Area covered
    Japan, United States
    Description

    This review of liquidity traps unifies three landmark economic downturns — the U.S. Great Depression, the Great Recession, and Japan’s Long Recession — into a single analytical framework. We examine various forces that drive natural interest rates negative: temporarily (such as banking crises and debt overhangs) or perma- nently (such as demographic shifts and inequality). When policy rates hit the zero lower bound, conventional monetary tools lose traction. Under a standard monetary policy regime, counterintuitive paradoxes emerge: greater price flexibility deepens recessions, and positive supply shocks become contractionary. We show how policy effects — including the size of fiscal multipliers, forward guidance, and these paradoxes — depend crit- ically on the monetary-fiscal regime and on central-bank credibility. The paper explains how regime changes, such as Roosevelt’s 1933 abandonment of the gold standard and balanced-budget dogmas, successfully re- versed deep slumps by credibly shifting expectations. We examine whether secular-stagnation forces are likely to assert themselves in the coming decades.

  15. Yield Curve and Predicted GDP Growth

    • clevelandfed.org
    csv
    Updated Oct 5, 2025
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    Federal Reserve Bank of Cleveland (2025). Yield Curve and Predicted GDP Growth [Dataset]. https://www.clevelandfed.org/indicators-and-data/yield-curve-and-predicted-gdp-growth
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    csvAvailable download formats
    Dataset updated
    Oct 5, 2025
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  16. Monthly bank rate in the UK 2012-2025

    • statista.com
    Updated Nov 27, 2025
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    Statista (2025). Monthly bank rate in the UK 2012-2025 [Dataset]. https://www.statista.com/statistics/889792/united-kingdom-uk-bank-base-rate/
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    Dataset updated
    Nov 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2012 - Oct 2025
    Area covered
    United Kingdom
    Description

    August 2024 marked a significant shift in the UK's monetary policy, as it saw the first reduction in the official bank base interest rate since August 2023. This change came after a period of consistent rate hikes that began in late 2021. In a bid to minimize the economic effects of the COVID-19 pandemic, the Bank of England cut the official bank base rate in March 2020 to a record low of *** percent. This historic low came just one week after the Bank of England cut rates from **** percent to **** percent in a bid to prevent mass job cuts in the United Kingdom. It remained at *** percent until December 2021 and was increased to one percent in May 2022 and to **** percent in October 2022. After that, the bank rate increased almost on a monthly basis, reaching **** percent in August 2023. It wasn't until August 2024 that the first rate decrease since the previous year occurred, signaling a potential shift in monetary policy. Why do central banks adjust interest rates? Central banks, including the Bank of England, adjust interest rates to manage economic stability and control inflation. Their strategies involve a delicate balance between two main approaches. When central banks raise interest rates, their goal is to cool down an overheated economy. Higher rates curb excessive spending and borrowing, which helps to prevent runaway inflation. This approach is typically used when the economy is growing too quickly or when inflation is rising above desired levels. Conversely, when central banks lower interest rates, they aim to encourage borrowing and investment. This strategy is employed to stimulate economic growth during periods of slowdown or recession. Lower rates make it cheaper for businesses and individuals to borrow money, which can lead to increased spending and investment. This dual approach allows central banks to maintain a balance between promoting growth and controlling inflation, ensuring long-term economic stability. Additionally, adjusting interest rates can influence currency values, impacting international trade and investment flows, further underscoring their critical role in a nation's economic health. Recent interest rate trends Between 2021 and 2025, most advanced and emerging economies experienced a period of regular interest rate hikes. This trend was driven by several factors, including persistent supply chain disruptions, high energy prices, and robust demand pressures. These elements combined to create significant inflationary trends, prompting central banks to raise rates to temper spending and borrowing. However, in 2024, a shift began to occur in global monetary policy. The European Central Bank (ECB) was among the first major central banks to reverse this trend by cutting interest rates. This move signaled a change in approach aimed at addressing growing economic slowdowns and supporting growth.

  17. f

    Nicaragua Economic Data

    • focus-economics.com
    excel, flat file, pdf
    Updated Jun 14, 2022
    + more versions
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    FocusEconomics S.L.U. (2022). Nicaragua Economic Data [Dataset]. https://www.focus-economics.com/countries/nicaragua
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    flat file, excel, pdfAvailable download formats
    Dataset updated
    Jun 14, 2022
    Authors
    FocusEconomics S.L.U.
    Time period covered
    1980 - 2028
    Area covered
    Nicaragua
    Description

    FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for Nicaragua.

  18. Data from: Monetary policy in Brazil in pandemic times

    • scielo.figshare.com
    tiff
    Updated Jun 1, 2023
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    Carmem Feijó; Eliane Cristina Araújo; Luiz Carlos Bresser-Pereira (2023). Monetary policy in Brazil in pandemic times [Dataset]. http://doi.org/10.6084/m9.figshare.19965335.v1
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    tiffAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Carmem Feijó; Eliane Cristina Araújo; Luiz Carlos Bresser-Pereira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    ABSTRACT The paper discusses the determination of inflation in Brazil, especially after the great recession of 2015-2016, to assess the adequacy of manipulating interest rates to control the rise in prices due to permanent cost pressure. The burden of using the interest rate to fight cost inflation is to create a highly conventional level of the real interest rate, which benefits the rentier class in a financialized economy. In the light of the post-Keynesian macroeconomics, a high-interest rate convention keeps the economy with a low growth rate and a low investment rate, which in the case of the Brazilian economy has resulted in a regression in the productive matrix and productivity stagnation, and both contribute to perpetuating cost pressures on prices. The empirical analysis corroborates the discussion about recent inflation having its origin in cost pressures over which the interest rate impact for its control is limited. We complement the empirical analysis by testing the response to the SELIC interest rate of the variables used to explain the fluctuation of market prices and administered prices: commodity price index, exchange rate and activity level. As expected, the impact of an increase in the interest rate appreciates the exchange rate, favouring inflation control and reducing the level of activity but has no impact on the commodity price index.

  19. Can Yield Curve Inversions Be Predicted?

    • clevelandfed.org
    Updated Jul 16, 2018
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    Federal Reserve Bank of Cleveland (2018). Can Yield Curve Inversions Be Predicted? [Dataset]. https://www.clevelandfed.org/publications/economic-commentary/2018/ec-201806-can-yield-curve-inversions-be-predicted
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    Dataset updated
    Jul 16, 2018
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    Description

    An inverted Treasury yield curve—a yield curve where short-term Treasury interest rates are higher than long-term Treasury interest rates—is a good predictor of recessions. Because of this, economists and policymakers often assess the risk of a yield curve inversion when the yield curve is flattening. I study the forecastability of yield curve inversions. Professional forecasters did not predict the beginning of the yield curve inversions prior to the 1990–1991, 2001, and 2008–2009 recessions. In all three cases, professional forecasters failed to predict the magnitude of the rise in short-term interest rates. Prior to the 2008–2009 recession, forecasters also overpredicted long-term interest rates.

  20. o

    Replication data for: A Simple Model of Subprime Borrowers and Credit Growth...

    • openicpsr.org
    Updated May 1, 2016
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    Alejandro Justiniano; Giorgio E. Primiceri; Andrea Tambalotti (2016). Replication data for: A Simple Model of Subprime Borrowers and Credit Growth [Dataset]. http://doi.org/10.3886/E116314V1
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    Dataset updated
    May 1, 2016
    Dataset provided by
    American Economic Association
    Authors
    Alejandro Justiniano; Giorgio E. Primiceri; Andrea Tambalotti
    Time period covered
    Jan 2000 - Dec 2006
    Area covered
    United States
    Description

    The surge in credit and house prices that preceded the Great Recession was particularly pronounced in ZIP codes with a higher fraction of subprime borrowers (Mian and Sufi, 2009). We present a simple model with prime and subprime borrowers distributed across geographic locations, which can reproduce this stylized fact as a result of an expansion in the supply of credit. Due to their low income, subprime households are constrained in their ability to meet interest payments and hence sustain debt. As a result, when the supply of credit increases and interest rates fall, they take on disproportionately more debt than their prime counterparts, who are not subject to that constraint.

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Shubhaansh Kumar (2023). US Recession Dataset [Dataset]. https://www.kaggle.com/datasets/shubhaanshkumar/us-recession-dataset
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US Recession Dataset

Navigating Economic Downturns: A Dataset of Key Indicators and Recession Binary

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zip(39062 bytes)Available download formats
Dataset updated
May 14, 2023
Authors
Shubhaansh Kumar
License

https://cdla.io/sharing-1-0/https://cdla.io/sharing-1-0/

Area covered
United States
Description

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

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