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This dataset contains the text from Federal Reserve FOMC (Federal Open Market Committee) meeting minutes and statements, collected by scraping the Federal Reserve's website. The data spans a specific period of time, providing insights into the central bank's monetary policy decisions and discussions.
The dataset consists of the following columns:
The data is collected from the official Federal Reserve website (https://www.federalreserve.gov) using a custom Python scraper built with BeautifulSoup.
This dataset can be used for various purposes, such as:
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The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This data set is a digitized version of “All-Bank Statistics, United States, 1896-1955,” (ABS) which the Board of Governors of the Federal Reserve System published in 1959. That volume contained annual aggregate balance sheet aggregates for all depository institutions by state and class of institution for the years 1896 to 1955. The depository institutions include nationally chartered commercial banks, state chartered commercial banks, and private banks as well as mutual savings bank and building and loan societies. The data comes from the last business day of the year or the closest available data. This digital version of ABS contains all data in the original source and only data from the original source.This data set is similar to ICPSR 2393, “U.S. Historical Data on Bank Market Structure, ICPSR 2393” by Mark Flood. ICPSR 2393 reports data from ABS but excludes subcategories of data useful for analyzing the liquidity of bank balance sheets, the operation of financial markets, the functioning of the financial network, and depository institutions’ contribution to monetary aggregates. ICPSR 2393, for example, reports total cash assets from ABS but does not report the subcomponents of that total: bankers balances, cash in banks’ own vaults, and items in the process of collection. Those data are needed to understand how much liquidity banks kept on hand, how much liquidity banks stored in or hoped to draw from reserve depositories, and how much of the apparent cash in the financial system was double-counted checks in the process of collection, commonly called float. Those data are also needed to understand the contribution of commercial banks to the aggregate money supply since cash in banks’ vaults counts within monetary aggregates while interbank deposits and float do not. While this dataset provides comprehensive and complete data from ABS, ICPSR 2393 contains information from other sources that researchers may find valuable including data from the aggregate income statements of nationally chartered banks and regulatory variables. To facilitate the use of that information, the naming conventions in this data set are consistent with those in ICPSR 2393.
The Agricultural Finance Databook is a compilation of various data on current developments in agricultural finance. Large portions of the data come from regular surveys conducted by the Board of Governors of the Federal Reserve System or by Federal Reserve Banks. Other portions come from the quarterly Call Report data of commercial banks or from the reports of other financial institutions involved in agricultural lending. This data is no longer published by the Federal Reserve Board. On October 1, 2010, the E.15 statistical release transitioned from the Board of Governors to the Federal Reserve Bank of Kansas City. You can now find the most current Agricultural Finance Databook at https://www.kansascityfed.org/research/indicatorsdata/agfinancedatabook.
Note: The Board of Governors has discontinued the Survey of Terms of Business Lending (STBL) and the associated E.2 release. The final STBL was conducted in May 2017, and the final E.2 was released on August 2, 2017. The STBL has been replaced by a new Small Business Lending Survey that commenced in February 2018. The new survey is being managed and administered by the Federal Reserve Bank of Kansas City. Results from this new survey can be found here.
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Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.
Data were previously published in the Supplement to the Federal Reserve Bulletin, which ceased publication in December 2008. These tables will be discontinued with the final table released in April 2022. The source for these data is the Treasury International Capital System and future data publications can be found on Treasury’s website.
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View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.
The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families balance sheets, pensions, income, and demographic characteristics. Information is also included from related surveys of pension providers and the earlier such surveys conducted by the Federal Reserve Board. No other study for the country collects comparable information. Data from the SCF are widely used, from analysis at the Federal Reserve and other branches of government to scholarly work at the major economic research centers.The survey has contained a panel element over two periods. Respondents to the 1983 survey were re-interviewed in 1986 and 1989. Respondents to the 2007 survey were re-interviewed in 2009.The study is sponsored by the Federal Reserve Board in cooperation with the Department of the Treasury. Since 1992, data have been collected by the National Opinion Research Center (NORC) at the University of Chicago.
The G.5 monthly release contains monthly average rates of exchange of major currencies against the U.S. dollar. Averages are based on daily noon buying rates for cable transfers in New York City certified for customs purposes by the Federal Reserve Bank of New York.
The Federal Reserve Banks provide the National Settlement Service (NSS), which allows participants in private-sector clearing arrangements to exchange and settle transactions on a multilateral basis through designated master accounts held at the Federal Reserve Banks. There are approximately 17 NSS arrangements that have been established by financial market utilities, check clearinghouse associations, and automated clearinghouse networks.NSS provides an automated mechanism for submitting settlement files to the Federal Reserve Banks and reduces settlement risk to participants by granting settlement finality on settlement day. NSS also enables the clearing arrangements to manage and limit settlement risk by incorporating risk controls that are as robust as those used in the Fedwire Funds Service. Participants generally submit settlement files online, by initiating an electronic message.
These tables are discontinued with the final table released in December 2013. The source for these data is the Treasury International Capital System and future data publications can be found on Treasurys website http://www.treasury.gov/resource-center/data-chart-center/tic/Pages/ticnonbank.aspx Data were previously published in the Supplement to the Federal Reserve Bulletin, which ceased publication in December 2008.
This spreadsheet contains the disaggregated national bank call reports by state and reserve city for each call report date. These data appear as compiled by the Comptroller of the Currency. These data are a “cleaned” version of the data published in the Annual Reports of the Comptroller of the Currency. Where assets and liabilities were not equal for a state or reserve city in the original, they have been corrected to be equal in this data set. This was done by comparing for each asset and liability category differences between totals as reported by the Comptroller and totals category obtained by aggregating the individual state and reserve city data. It should also be noted that aggregates for the entire National Banking System should be based on the individual data in this dataset and not those reported by the Comptroller. After 1900 the dates for the data for Alaska and Hawaii that the Comptroller used in his totals do not match the dates given in the individual state reports.
The data presented in this Enhanced Financial Accounts (EFA) project provide additional detail on funding agreement-backed securities (FABS). FABS are securities that are backed by a funding agreement, which is a deposit-type contract, issued by life insurance companies, that promises a stream of predictable fixed payments over a specified period of time.
Dodd-Frank Act stress testing (DFAST)--a complementary exercise to the Comprehensive Capital Analysis and Review (CCAR)--is a forward-looking initiative conducted by the Federal Reserve and financial companies supervised by the Federal Reserve to help assess whether institutions have sufficient capital to absorb losses and support operations during adverse economic conditions.
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Dataset Description
This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.
The dataset includes the following five columns:
Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.
Data Usage
This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality
The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.
Data Limitations
The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.
The FR 3075 collects information from certain types of institutions regulated by the Board in order to assess the effects of proposed, pending, or recently adopted policy changes at the domestic and international levels. The Board uses the survey to collect information used for certain quantitative impact studies (QISs) sponsored by financial stability bodies such as the Basel Committee on Banking Supervision (BCBS) and the Financial Stability Board (FSB). Recent collections have included the Basel III monitoring exercise, which monitors the global impact of the Basel III framework, the global systemically important bank (G-SIB) exercise, which assesses firms’ systemic risk profiles, and a survey of the domestic systemic risk footprint of large foreign banking organizations. Since the collected data may change from survey to survey, there is no fixed reporting form. The surveys are conducted on a voluntary basis. The number of respondents per survey and the number of surveys conducted per year fluctuate.
This dataset sheds light on the evolution of published contributions of women economists over a 110-year period. The dataset is collected from the Papers and Proceedings (P&P), an annual volume of the proceedings of the American Economic Association (AEA) annual meetings, which has been published since 1911. It catalogs the 110 years of publications in the annual proceedings volume and provides insight into women's position in the economics profession, as well as differences between men and women with respect to coauthoring patterns, institutional affiliations, and research fields.
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Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.
In response to the COVID-19 crisis, the Board's emergency lending facilities have provided a critical backstop. The Board launched a centralized 13(3) Lending Facilities Data Repository on November 6, 2020 to bring together the emergency lending facilities data from different systems and databases. The Federal Reserve established the Term Asset-Backed Securities Loan Facility (TALF) on March 23, 2020 to support the flow of credit to consumers and businesses. The TALF will enable the issuance of asset-backed securities (ABS) backed by student loans, auto loans, credit card loans, loans guaranteed by the Small Business Administration (SBA), and certain other assets. Under the TALF, the Federal Reserve will lend on a non-recourse basis to holders of certain AAA-rated ABS backed by newly and recently originated consumer and small business loans. The Federal Reserve will lend an amount equal to the market value of the ABS less a haircut and will be secured at all times by the ABS. Treasury, using the ESF, will also make an equity investment in the SPV established by the Federal Reserve for this facility. The TALF ceased extending credit on December 31, 2020.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset contains the text from Federal Reserve FOMC (Federal Open Market Committee) meeting minutes and statements, collected by scraping the Federal Reserve's website. The data spans a specific period of time, providing insights into the central bank's monetary policy decisions and discussions.
The dataset consists of the following columns:
The data is collected from the official Federal Reserve website (https://www.federalreserve.gov) using a custom Python scraper built with BeautifulSoup.
This dataset can be used for various purposes, such as: