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 Mexico was last recorded at 8.50 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The benchmark interest rate in Brazil was last recorded at 14.75 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We examine when government debt crowds out investment for the US economy using an estimated New Keynesian model with detailed fiscal specifications and accounting for monetary and fiscal policy interactions. Whether investment is crowded in or out in the short term depends on policy shocks triggering debt expansions: higher debt can crowd in investment for cutting capital tax rates or increasing government investment. Contrary to the conventional view, no systematic relationships between real interest rates and investment exist, explaining why reduced-form regressions are inconclusive about crowding out. At longer horizons, distortionary financing is important for the negative investment response to debt.

Dominican Republic DO: Interest Rate Spread data was reported at 7.903 % pa in 2016. This records a decrease from the previous number of 8.316 % pa for 2015. Dominican Republic DO: Interest Rate Spread data is updated yearly, averaging 8.865 % pa from Dec 1996 (Median) to 2016, with 21 observations. The data reached an all-time high of 11.517 % pa in 2004 and a record low of 7.176 % pa in 2014. Dominican Republic DO: Interest Rate Spread data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.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;

An important indicator of the financial strength of governmental entity is its bond rating. The bond rating is similar in nature to the credit score of an individual – the higher the score, the better the ability to borrow money to finance purchases at a lower interest rate. Similarly, the higher the bond rating for a governmental entity, the more opportunities to borrow money for capital needs at lower interest rates. A high bond rating is in excellent indicator of the overall financial health of a government.This measure is obtained each year when the city seeks to issue bonds to finance its’ projects. As part of this process, bond ratings are always obtained from the rating agencies: Standard & Poor’s. Fitch Ratings and Moody's Investor Service.This page provides data for the Bond Rating performance measure.Bond ratings are a reflection of the financial strength of an entity. A high rating means an entity can issue bonds to finance capital projects at lower interest rates; lower rates result in less interest to be paid on the repayment of the bonds. Ultimately, this lowers the costs of our capital projects to our taxpayers.The performance measure dashboard is available at 5.04 Bond Rating.Additional InformationSource: Standard & Poors, Moody's Investor Service, and Fitch Ratings are the major bond rating agencies in the United States and are widely used by governmental and non-governmental entities throughout the country.Contact: Jerry HartContact E-Mail: Jerry_Hart@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

Antigua and Barbuda AG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 6.997 % pa in 2018. This records an increase from the previous number of 6.645 % pa for 2017. Antigua and Barbuda AG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 4.624 % pa from Dec 1982 (Median) to 2018, with 37 observations. The data reached an all-time high of 6.997 % pa in 2018 and a record low of 2.380 % pa in 1988. Antigua and Barbuda AG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Antigua and Barbuda – Table AG.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.;International Monetary Fund, International Financial Statistics database.;;

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

MX: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 0.653 % pa in 2017. This records an increase from the previous number of 0.570 % pa for 2016. MX: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 0.884 % pa from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 10.993 % pa in 1995 and a record low of 0.319 % pa in 2006. MX: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

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.

We study how model uncertainty affects the understanding of the interest rate persistence using a generalized Taylor-rule function covering numerous submodels via model average approach. The data-driven weights can be regarded as a measure of power-sharing across monetary policy committee members. We show that the model uncertainty is important in Canada, France, and Sweden, and the implied weights indicate that the U.K. and the U.S. have a lower model uncertainty caused either by an over-influential chairman or the consistent agreement of committee members. The importance of model uncertainty can be emphasized by sequential estimation during the 2008 financial crisis.

Based on a large historical panel dataset, this paper provides evidence that the government spending multiplier can be significantly higher when interest rates are at or near the zero lower bound (ZLB). We estimate multipliers that are as high as 1.5 during ZLB episodes but small and statistically indistinguishable from zero during normal times. Our results are robust to different definitions of ZLB episodes, alternative ways of identifying government spending shocks, controlling for the exchange rate regime, and other potentially important state variables. In particular, we show that the difference in multipliers is not driven by multipliers being higher during periods of economic slack.

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

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.

The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Zimbabwe ZW: Real Interest Rate data was reported at 5.728 % pa in 2016. This records a decrease from the previous number of 7.576 % pa for 2015. Zimbabwe ZW: Real Interest Rate data is updated yearly, averaging 34.675 % pa from Dec 1980 (Median) to 2016, with 32 observations. The data reached an all-time high of 572.936 % pa in 2007 and a record low of 4.257 % pa in 1980. Zimbabwe ZW: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement

We build and estimate an equilibrium model of the term structure of interest rates based on a recursive utility specification. We contrast it with an arbitrage-free model, where prices of risk are estimated freely without preference constraints. In both models, nominal bond yields are affine functions of macroeconomic state variables. The equilibrium model accounts for the tent-shaped pattern and magnitude of coefficients from predictive regressions of excess bond returns on forward rates and the hump-shaped pattern in the term structure of volatilities, while the reduced-form no-arbitrage model does not account for these important features of the yield curve.