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.

United States Interest Rates: 12 Months Expectation: Lower data was reported at 21.400 % in Apr 2025. This records a decrease from the previous number of 23.300 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Lower data is updated monthly, averaging 12.100 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 45.800 % in Jan 1991 and a record low of 5.200 % in Jun 2018. United States Interest Rates: 12 Months Expectation: Lower data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.

The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

Geographic coverage

Global Survey, covering 108 countries, representing 88 percent of global GDP.

Analysis unit

Regulation at the national level.

Universe

Banking supervisors and Local Banking Associations.

Kind of data

Sample survey data [ssd]

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Bank supervisors and banking associations were provided with a standard excel file with five parts. The survey was structured in five parts, each placed in a different excel sheet. Part A: Introduction. Countries with no IRCs in place were asked to only answer this sheet and leave the rest blank. Part B: Presented the definitions of controls, institutions, products and additional aspects that will be covered in the survey. Part C: Introduced a set of qualitative questions to describe the IRCs in place. Part D: Displayed a set of tables to quantitatively describe the IRCs in place. Part E: Laid out the final set of questions, covering sanctions and control mechanisms that support the IRCs' enforcement. The questionnaire is provided in the Documentation section in pdf and excel.

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.

This dataset supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

A traditional way of thinking about the exchange rate regime and capital account openness has been framed in terms of the 'impossible trinity' or 'trilemma', according to which policymakers can only have two of three possible outcomes: open capital markets, monetary independence and pegged exchange rates. The present paper is a natural extension of Escude (A DSGE Model for a SOE with Systematic Interest and Foreign Exchange Policies in Which Policymakers Exploit the Risk Premium for Stabilization Purposes, 2013), which focuses on interest rate and exchange rate policies, since it introduces the third vertex of the 'trinity' in the form of taxes on private foreign debt. These affect the risk-adjusted uncovered interest parity equation and hence influence the SOE's international financial flows. A useful way to illustrate the range of policy alternatives is to associate them with the faces of an isosceles triangle. Each of three possible government intervention policies taken individually (in the domestic currency bond market, in the foreign currency market, and in the foreign currency bonds market) corresponds to one of the vertices of the triangle, each of the three possible pairs of intervention policies corresponds to one of the three edges of the triangle, and the three simultaneous intervention policies taken jointly correspond to the triangle's interior. This paper shows that this interior, or 'pos sible trinity' is quite generally not only possible but optimal, since the central bank obtains a lower loss when it implements a policy with all three interventions.

Graph and download economic data for Interest Rates, Discount Rate for United States (INTDSRUSM193N) from Jan 1950 to Aug 2021 about discount, interest rate, interest, rate, and USA.

United States Interest Rates: 12 Months Expectation: Higher data was reported at 56.100 % in Apr 2025. This records an increase from the previous number of 53.400 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Higher data is updated monthly, averaging 55.200 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 79.900 % in Mar 1989 and a record low of 23.400 % in Oct 2001. United States Interest Rates: 12 Months Expectation: Higher data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

We apply a discrete choice approach to model the empirical behaviour of the Federal Reserve in changing the federal funds target rate, the benchmark of short-term market interest rates in the US. Our methods allow the explanatory variables to be nonstationary as well as stationary. This feature is particularly useful in the present application as many economic fundamentals that are monitored by the Fed and are believed to affect decisions to adjust interest rate targets display some nonstationarity over time. The chosen model successfully predicts the majority of the target rate changes during the time period considered (1994-2001) and helps to explain strings of similar intervention decisions by the Fed. Based on the model-implied optimal interest rate, our findings suggest that there is a lag in the Fed's reaction to economic shocks during this period.

The dataset contains several macroeconomic time-series regarding the Russian economy. The time-series were collected from the Russian Federal State Statistics Service, the Bank of Russia and Federal Reserve Economic Data. The time-series included in the dataset are: 1. Time: 1-Jan-2005 = 1, every successive step in time represents one quarter 2. Date: Quarterly dates from 1-Jan-2005 to 1-Oct-2021 5. GDP: Quarterly nominal GDP in 2016 prices, excluding seasonal factor (bln RUB) 6. GDPgr: Nominal GDP growth rate (Quarterly, %) 7. M0: Base or high-powered money (bln RUB) 8. M0gr: M0 growth rate (Quarterly, %) 9. BM: M2 measure of money supply (bln RUB) 10. BMgr: M2 growth rate (Quarterly, %) 11. Interest: 90-day interbank rate (APR, %) 12. USDRUB: USD/RUB exchange rate (RUB) 12. EURRUB: EUR/RUB exchange rate (RUB) 13. Unemployment: Unemployment rate (%) 14. PPI: Domestic producer price index (index: 2015=100) 15. PPIgr: Growth rate of producer price index (Quarterly, %) 16. OIL: Spot prices of Brent per barrel (USD) 17. OILgr: Growth rate of Brent prices (Quarterly, %) 18. WAGE: Average monthly nominal wage rate (RUB) 19. WAGEgr: Changes in nominal wage rate (Quarterly, %) 3. CPI: Change in CPI as a ratio (End of quarter to end of previous quarter, %) 4. Inflation: Percentage change in CPI, calculated as Relative CPI - 100 (Quarterly, %)

The data was used to in time-series regression modelling to explain the factors affecting inflation in Russia. Some other modelling ideas for the dataset are: 1. Shift the focus from factor analysis to predicting future inflation 2. Perform factor analyses of other key macroeconomic variables, such as the GDP growth rate, the unemployment rate or the interest rate

Due to the low number of available observations because of quarterly sampling, this dataset is probably better suited to time-series econometric analysis rather than more modern machine learning methods.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

A time-varying parameter model with Markov-switching conditional heteroscedasticity is employed to investigate two sources of shifts in real interest rates: (1) shifts in the coefficients relating the ex ante real rate to the nominal rate, the inflation rate and a supply shock variable and (2) unconditional shifts in the variance of the stochastic process. The results underscore the importance of modelling continual change in the ex ante real rate in terms of other economic variables rather than relying on a statistical characterization that permits only a limited number of discrete jumps in the mean of the process.

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 Japan Interest Rate.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Content

The STLFSI measures the degree of financial stress in the markets and is constructed from 18 weekly data series: seven interest rate series, six yield spreads and five other indicators. Each of these variables captures some aspect of financial stress. Accordingly, as the level of financial stress in the economy changes, the data series are likely to move together.

How to Interpret the Index: The average value of the index, which begins in late 1993, is designed to be zero. Thus, zero is viewed as representing normal financial market conditions. Values below zero suggest below-average financial market stress, while values above zero suggest above-average financial market stress.

More information: For additional information on the STLFSI and its construction, see "Measuring Financial Market Stress" (https://files.stlouisfed.org/research/publications/es/10/ES1002.pdf) and the related appendix (https://files.stlouisfed.org/files/htdocs/publications/net/NETJan2010Appendix.pdf).

See this list (https://www.stlouisfed.org/news-releases/st-louis-fed-financial-stress-index/stlfsi-key) of the components that are used to construct the STLFSI.

As of 07/15/2010 the Vanguard Financial Exchange-Traded Fund series has been replaced with the S&P 500 Financials Index. This change was made to facilitate a more timely and automated updating of the FSI. Switching from the Vanguard series to the S&P series produced no meaningful change in the index.

Copyright, 2016, Federal Reserve Bank of St. Louis.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1993-12-31

• Observation End : 2019-11-29

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Laura Lefurgey-Smith on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

A traditional way of thinking about the exchange rate regime and capital account openness has been framed in terms of the 'impossible trinity' or 'trilemma', according to which policymakers can only have two of three possible outcomes: open capital markets, monetary independence and pegged exchange rates. The present paper is a natural extension of Escude (A DSGE Model for a SOE with Systematic Interest and Foreign Exchange Policies in Which Policymakers Exploit the Risk Premium for Stabilization Purposes, 2013), which focuses on interest rate and exchange rate policies, since it introduces the third vertex of the 'trinity' in the form of taxes on private foreign debt. These affect the risk-adjusted uncovered interest parity equation and hence influence the SOE's international financial flows. A useful way to illustrate the range of policy alternatives is to associate them with the faces of an isosceles triangle. Each of three possible government intervention policies taken individually (in the domestic currency bond market, in the foreign currency market, and in the foreign currency bonds market) corresponds to one of the vertices of the triangle, each of the three possible pairs of intervention policies corresponds to one of the three edges of the triangle, and the three simultaneous intervention policies taken jointly correspond to the triangle's interior. This paper shows that this interior, or 'pos sible trinity' is quite generally not only possible but optimal, since the central bank obtains a lower loss when it implements a policy with all three interventions.