The benchmark interest rate in the United States was last recorded at 4.25 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.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

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.

Fixed 30-year mortgage rates in the United States averaged 6.46 percent in the week ending September 26 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Senegal SN: Interest Rate Spread data was reported at -1.630 % pa in 2016. This records an increase from the previous number of -1.844 % pa for 2015. Senegal SN: Interest Rate Spread data is updated yearly, averaging -3.008 % pa from Dec 2005 (Median) to 2016, with 12 observations. The data reached an all-time high of -0.558 % pa in 2005 and a record low of -3.602 % pa in 2009. Senegal SN: Interest Rate Spread data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Senegal – Table SN.World Bank.WDI: Interest Rates. Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; Median;

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

The benchmark interest rate in Canada was last recorded at 2.50 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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]

The benchmark interest rate in Turkey was last recorded at 40.50 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

The benchmark interest rate in Indonesia was last recorded at 4.75 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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When examining the intricate relationship between economic conditions and purchasing decisions, the utilization of practice datasets can offer invaluable insights. This particular artificial dataset comprises three main components: a dimension table of ten companies, a fact table documenting purchases from these companies, and a set of data points regarding economic conditions. These elements are meticulously designed to mimic real-world scenarios, enabling analysts to dissect and understand how fluctuations in the economy can influence the purchasing behavior of different types of companies.

The dimension table serves as the foundation, listing ten distinct companies, each potentially operating in varied sectors. This diversity allows for a comprehensive analysis across a spectrum of industries, highlighting sector-specific sensitivities to economic changes. The fact table of purchases acts as a historical record, offering detailed insights into the buying patterns of these companies over time. Analysts can observe trends, frequencies, and the magnitude of purchases, correlating them with the economic conditions presented in the third component of the dataset.

The economic conditions data is pivotal, as it encompasses a variety of indicators that can affect purchasing decisions. These may include inflation rates, interest rates, GDP growth, unemployment rates, and consumer confidence indices, among others. By examining the interplay between these economic indicators and the purchasing data, analysts can identify patterns and causations. For instance, an increase in interest rates might lead to a decrease in capital-intensive purchases by companies wary of higher borrowing costs.

Through this dataset, researchers can employ statistical models and data analysis techniques to uncover how economic fluctuations impact corporate purchasing decisions. The findings can offer valuable lessons for businesses in terms of budgeting, financial planning, and risk management. Companies can use these insights to make informed decisions, adjusting their purchasing strategies in anticipation of or in response to economic conditions. This proactive approach can help businesses maintain stability during economic downturns and capitalize on opportunities during favorable economic times.

Ultimately, this practice dataset not only aids in academic and educational pursuits but also serves as a practical tool for business analysts, economists, and corporate strategists seeking to better navigate the complex dynamics of the economy and its effects on corporate purchasing behaviors.

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.

Machine Learning Predicts QQQ to Increase in Value by 5% in the Next 3 Months

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

Abstract copyright UK Data Service and data collection copyright owner.The Survey of Mortgage Lenders (SML) was launched on 1st April 1992 to succeed the 5% Sample Survey of Building Society Mortgage Completions (BSM) (See GN:33191). The aims were to improve the survey in three principal ways: a) to broaden the range of institutions surveyed to incorporate other mortgage lenders in addition to building societies and Abbey National. With the entry of the high street banks and then the centralised lenders into the mortgage market, information provided by the building societies no longer represented the whole market in the way it did when the BSM was set up in the 1960s. b) to extend its coverage to include further advances, remortgages and top-up loans in addition to first mortgages. c) to increase the level of detail on the questionnaire especially with respect to the characteristics of the mortgage loan. An important consideration for users of the data is that the SML figures allow continuity with the BSM survey results to be maintained for a reasonable period. Main Topics: Financial institution code, date mortgage completed, whether dwelling is wholly or partly occupied by borrower. Mortgage amount, type of advance, whether solely for purchase of property, period of mortgage, gross rate of interest, whether the interest charged is fixed or variable rate, whether interest payments are discounted or deferred, repayment method, source of mortgage business, purchase price and whether discounted in any way, location of dwelling, whether new, age of dwelling, type of dwelling, number of habitable rooms, number, sex and age of borrowers, basic income of main borrower, other income and total income on which mortgage is based, whether applicant previously owner occupier, previous tenure. The institutions are divided into four strata according to the size of their assets. All the largest were asked to complete questionnaires on a sample of 5 per cent of their new mortgage advances. Mortgages are included if their reference numbers end in specified digits chosen so that every twentieth mortgage is selected. Institutions in the next stratum are arranged in order of size of assets and alternate institutions chosen each of which are asked to complete questionnaires on 10 per cent of their mortgages. In the next stratum 20 per cent of the mortgages of every fourth institution are obtained. The smallest institutions are completely excluded.

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.

The benchmark interest rate in Norway was last recorded at 4 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.