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.

According to several empirical studies US inflation and nominal interest rates as well as the real interest rate can be described as unit root processes. These results imply that nominal interest rates and expected inflation do not move one-for-one in the long run, which is incongruent with theoretical models. In this paper we introduce a new nonlinear bivariate mixture autoregressive model that seems to fit quarterly US data (1953 : II?2004 : IV) reasonably well. It is found that the three-month Treasury bill rate and inflation share a common nonlinear component that explains a large part of their persistence. The real interest rate is devoid of this component, indicating one-for-one movement of the nominal interest rate and inflation in the long run and, hence, stationarity of the real interest rate.

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.

The Banking System in India is very diversified and to compare between the interest rates of different banks is tedious.So here is the dataset to make it easy for you. About the dataset: 1.Bank Name:Name of the Bank 2.Highest Slab: Maximum Interest Rate that the bank can provide 3.1 year tenure : The interest rate given on FD for a time limit of 1 year 4.3 year tenure: The interest rate given on FD for a time limit of 3 years 5.5 year tenure:The interest rate given on FD for a time limit of 5 years. 6.Senior Citizens:The interest rate given on a FD for the senior citizens above age 60.

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

Tax interest is compounded daily and interest rates are reset every 3 months.

Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the "https://www.fin.gov.on.ca/en/consultations/landtaxreform/payment-forms.html">provincial land tax webpage. Interest rates do not apply to the Estate Administration Tax Act, 1998.

Current interest rates (April 1, 2025 to June 30, 2025):

• 8% on taxes you owe to the ministry
• 2% on taxes you overpaid
• 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection
• 6% on late International Fuel Tax Agreement payments
• 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days

You can download the dataset to view the historical tax interest rates.

Non-Resident Speculation Tax (NRST)

(1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid.

(2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

Historical 3-Month Treasury Bill Rates (2000-2023) Dataset

Track the Pulse of the Financial Market with Detailed T-Bill Data

Dataset Description

This dataset contains historical 3-month Treasury Bill rates, sourced from Yahoo Finance. The dataset spans from January 3, 2000, to December 31, 2023, and provides daily prices along with adjusted close prices and volumes. This data is crucial for financial analysts, economists, and researchers who are interested in interest rate trends and their impact on the economy.

Table of Contents

1. Introduction
2. Data Source
3. Dataset Structure
4. Data Collection and Processing
5. Usage and Applications
6. License
7. Acknowledgements

Introduction

Treasury Bills (T-Bills) are short-term government securities with maturities of one year or less. They are sold at a discount from their face value and do not pay interest before maturity. This dataset specifically focuses on the 3-month T-Bill rates, which are commonly used as a risk-free rate benchmark in various financial models and analyses.

The 3-month T-Bill rate is considered a reliable indicator of short-term interest rates and economic conditions. It is widely used in the valuation of financial instruments, risk management, and macroeconomic analysis.

Data Source

The data was sourced from Yahoo Finance. The Ticker symbol used for the 3-month Treasury Bill rates is ^IRX.

Dataset Structure

The dataset is provided in CSV format with the following columns:

• Date: The date of the recorded interest rate (in YYYY-MM-DD format).
• Open: The opening price of the 3-month T-Bill rate on the given date.
• High: The highest price of the 3-month T-Bill rate on the given date.
• Low: The lowest price of the 3-month T-Bill rate on the given date.
• Close: The closing price of the 3-month T-Bill rate on the given date.
• Adj Close: The adjusted close price for the 3-month T-Bill rate, adjusted for any corporate actions.
• Volume: The trading volume of the 3-month T-Bill rate on the given date (although typically 0 for T-Bills).

Example:

Data Collection and Processing

The data was collected from Yahoo Finance using the Python yfinance library. The following steps were performed to process the data:

1. Data Retrieval: Historical data was fetched for the 3-month Treasury Bill using the yfinance API.
2. Data Cleaning: Missing values were dropped to ensure data consistency.
3. Conversion: The adjusted close prices were used for analysis as they account for any corporate actions.

Usage and Applications

This dataset can be used for various financial analyses and modeling, including but not limited to:

• Interest Rate Modeling: Use the dataset to model interest rate behaviors using stochastic models such as the Vasicek model, Cox-Ingersoll-Ross model, etc.
• Risk Management: Assess the risk-free rate for evaluating investment performance and calculating risk premiums.
• Econometric Analysis: Conduct time series analysis to study trends, seasonality, and economic cycles.
• Financial Education: Serve as a resource for educational purposes in finance and economics courses.
• Macroeconomic Research: Analyze the relationship between short-term interest rates and macroeconomic indicators such as GDP growth, inflation, and employment rates.

License

This dataset is made available under the Creative Commons Attribution 4.0 International License. You are free to use, modify, and distribute the data, provided proper attribution is given.

Acknowledgements

Special thanks to Yahoo Finance for providing the historical data and the Python community for the yfinance library, which facilitated data retrieval and processing.

The benchmark interest rate in Norway was last recorded at 4.50 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 the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.; ;

Iran IR: Lending Interest Rate data was reported at 18.000 % pa in 2016. This records an increase from the previous number of 14.210 % pa for 2015. Iran IR: Lending Interest Rate data is updated yearly, averaging 12.000 % pa from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 18.000 % pa in 2016 and a record low of 11.000 % pa in 2013. Iran IR: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

Nepal NP: Real Interest Rate data was reported at -6.207 % pa in 2010. This records an increase from the previous number of -6.823 % pa for 2009. Nepal NP: Real Interest Rate data is updated yearly, averaging 3.657 % pa from Dec 1975 (Median) to 2010, with 29 observations. The data reached an all-time high of 18.214 % pa in 1977 and a record low of -12.173 % pa in 1975. Nepal NP: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.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.; ;

Current Deposit & Loan Rates - These rates are compiled from information reported by the commercial banks to the Economic Information and Publications Department. The rates of interest being offered on time deposits relate to amounts J$100,000 and over. The savings rate represents an average range of rates offered on all categories of savings deposits. The average lending rate is a simple average of the range of interest rates offered on demand loans only.

Domestic Interest Rates (Commercial Banks Weighted Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Domestic Interest Rates (Commercial Banks Weighted Time Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Domestic Interest Rates (Commercial Banks Weighted Loan Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Foreign Currency Interest Rates (Commercial Banks Weighted Time Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all foreign currency deposits and loans extended at non zero rates of interest.

Foreign Currency Interest Rates (Commercial Banks Weighted Loan Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all foreign currency deposits and loans extended at non zero rates of interest.

Comparative Bank Rates & Treasury Bill Rates - The average discount rate on three-month Treasury Bills or six month Treasury Bills in the case of Jamaica. The average discount rates for respective countries are sourced from the International Financial Statistics, an International Monetary Fund publication.

Private Money Markets Interest Rates

BOJ Interest Rates On Lending Facilities For DTI's - These interest rates fall under the Enhanced Liquidity Management Framework (ELMF), which was implemented by the Bank in 2013, for DTI.

Source: http://boj.org.jm/statistics/econdata/stats_list.php?type=5

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 ---

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

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.

Bangladesh BD: Lending Interest Rate data was reported at 9.852 % pa in 2024. This records an increase from the previous number of 7.570 % pa for 2023. Bangladesh BD: Lending Interest Rate data is updated yearly, averaging 12.219 % pa from Dec 1976 (Median) to 2024, with 49 observations. The data reached an all-time high of 14.846 % pa in 1990 and a record low of 7.121 % pa in 2022. Bangladesh BD: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.;International Monetary Fund, International Financial Statistics and data files.;;

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.801 % in Sep 2024. This records an increase from the previous number of 6.798 % for Jun 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.310 % from Mar 2012 (Median) to Sep 2024, with 51 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]

This dataset provides information about people applying for loans, including details on their personal background, finances, and loan specifics. It's meant to help us better understand how different personal factors impact whether a loan gets approved. The data includes things like the applicant's age, income, home ownership status, job history, and credit score, along with loan details such as the loan amount, interest rate, and purpose. It also shows whether the loan was approved or denied.

Features in the dataset:

1. person_age: The applicant's age.
2. person_income: How much the applicant earns annually, in USD.
3. person_home_ownership: Whether the person owns, rents, or has a mortgage on their home.
4. person_emp_length: How long the person has been employed.
5. loan_intent: The reason for applying for the loan (e.g., EDUCATION, MEDICAL, VENTURE, DEBT CONSOLIDATION, PERSONAL).
6. loan_grade: The credit grade given to the loan (A, B, C, D, etc.).
7. loan_amnt: The amount of money the applicant is requesting for the loan, in USD.
8. loan_int_rate: The interest rate on the loan.
9. loan_percent_income: The percentage of the applicant’s income being requested as a loan.
10. cb_person_default_on_file: Whether the applicant has defaulted on any previous loans (Y/N).
11. cb_person_cred_hist_length: The length of the applicant's credit history, in years.
12. loan_status: The outcome of the loan application (0 = Rejected, 1 = Accepted).