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.

The benchmark interest rate in Sweden was last recorded at 2 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 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]

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Public Debt and Low Interest Rates, PIIE Working Paper 19-4. If you use the data, please cite as: Blanchard, Olivier. (2019). Public Debt and Low Interest Rates. PIIE Working Paper 19-4. Peterson Institute for International Economics.

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.

Graph and download economic data for 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Aug 2025 about 10-year, interest rate, interest, real, rate, and USA.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

An important indicator of the financial strength of a governmental entity is its bond rating. The bond rating is similar 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 an 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 Information Source: 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

This dataset contains a wealth of information from 52,000 loan applications, offering detailed insights into the factors that influence loan approval decisions. Collected from financial institutions, this data is highly valuable for credit risk analysis, financial modeling, and predictive analytics. The dataset is particularly useful for anyone interested in applying machine learning techniques to real-world financial decision-making scenarios.

Overview: This dataset provides information about various applicants and the loans they applied for, including their demographic details, income, loan terms, and approval status. By analyzing this data, one can gain an understanding of which factors are most critical for determining the likelihood of loan approval. The dataset can also help in evaluating credit risk and building robust credit scoring systems.

Dataset Columns: Applicant_ID: Unique identifier for each loan application. Gender: Gender of the applicant (Male/Female). Age: Age of the applicant. Marital_Status: Marital status of the applicant (Single/Married). Dependents: Number of dependents the applicant has. Education: Education level of the applicant (Graduate/Not Graduate). Employment_Status: Employment status of the applicant (Employed, Self-Employed, Unemployed). Occupation_Type: Type of occupation, which provides insights into the nature of the applicant’s job (Salaried, Business, Others). Residential_Status: Type of residence (Owned, Rented, Mortgage). City/Town: The city or town where the applicant resides. Annual_Income: The total annual income of the applicant, a key factor in loan eligibility. Monthly_Expenses: The monthly expenses of the applicant, indicating their financial obligations. Credit_Score: The applicant's credit score, reflecting their creditworthiness. Existing_Loans: Number of existing loans the applicant is servicing. Total_Existing_Loan_Amount: The total amount of all existing loans the applicant has. Outstanding_Debt: The remaining amount of debt yet to be paid by the applicant. Loan_History: The applicant’s previous loan history (Good/Bad), indicating their repayment reliability. Loan_Amount_Requested: The loan amount the applicant has applied for. Loan_Term: The term of the loan in months. Loan_Purpose: The purpose of the loan (e.g., Home, Car, Education, Personal, Business). Interest_Rate: The interest rate applied to the loan. Loan_Type: The type of loan (Secured/Unsecured). Co-Applicant: Indicates if there is a co-applicant for the loan (Yes/No). Bank_Account_History: Applicant’s banking history, showing past transactions and reliability. Transaction_Frequency: The frequency of financial transactions in the applicant’s bank account (Low/Medium/High). Default_Risk: The risk level of the applicant defaulting on the loan (Low/Medium/High). Loan_Approval_Status: Final decision on the loan application (Approved/Rejected).

The benchmark interest rate in Mexico was last recorded at 7.75 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Taiwan was last recorded at 2 percent. This dataset provides the latest reported value for - Taiwan Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This repository hosts the The Guardian Reading Dataset, designed to explore readers' experiences with textual complexity, comprehensibility, and interest. The dataset captures detailed subjective and objective measures of readers' interactions with a selection of articles from The Guardian, providing granular insights into how textual features impact reading engagement.DescriptionThis dataset includes data from 30 readers who participated in 540 reading sessions. Each participant evaluated 18 articles sampled at three levels of textual complexity (low, medium, high), determined by a readability algorithm (Van der Sluis, 2014). The data captures subjective appraisals of complexity, comprehensibility, and interest, alongside eye-tracking metrics to provide an objective view of readers' processing difficulty and engagement with the text.Data File and StructureData is stored in .csv format at the reading session level, with each row corresponding to a unique reading session by a participant. Participant identifiers, demographic scales, and trait measures were dropped as part of anynomisation. Key columns are:stimulus: Identifier for each article.appraised_complexity: Participant’s perceived complexity of the article.appraised_comprehensibility: Participant’s perceived comprehensibility of the article.processing_fluency: Combined score of appraised complexity and appraised comprehensibility.interest: Participant’s interest rating for the article.topical_familiarity: Participant’s familiarity with the article’s topic.familiarity_group:Grouping of articles into balanced blocks (either low, median, or high familiarity).pupil_diameter: Average pupil diameter across the reading session, reflecting cognitive load during reading.pupil_corrected: Baseline-corrected pupil diameter per reading session (details on preprocessing and correction can be found in Van der Sluis et al., 2023).novelty_comprehensibility_scale_*: Semantic differentials, rating the content as complex – simple (57), not familiar to me – very familiar to me, easy to read – difficult to read(59), easy to understand – hard to understand (60), comprehensible – incomprehensible (61), coherent – incoherent (62), interesting – uninteresting (63), boring – exciting (64). Of these, 59, 60, 61, 62, and 63 were reverse scored in the resulting scales.fam_answer_*: Participants' topical familiarity rating for five topics per article. Note that each column covers different topics depending on the article.Data is stored in .db format at the stimulus (text) level, with each row corresponding to a unique text. In addition to averaged measurements aggregated per article, key columns are:text: Article content excerpt (first 50 and last 50 words of excerpt presented to study participants).url: Source URL for full text access.PurposeThe purpose of this dataset is to facilitate the analysis of human responses to differences in textual complexity, with a focus on understanding how readers' interest varies with different complexity levels. The controlled conditions and validated data in this dataset make it ideal for assessing the accuracy and applicability of models of textual complexity, ensuring that the findings are both reliable and relevant to actual readers' perceptions and experiences.LicensingThe dataset contains textual excerpts and metadata from The Guardian, shared under The Guardian’s open license terms (https:/www.theguardian.com/info/2022/nov/01/open-licence-terms). Full-text sharing is restricted, but excerpts of up till 100 words may be used with proper attribution.ReferencesVan der Sluis, F., & van den Broek, E. L. (2023). Feedback beyond accuracy: Using eye-tracking to detect comprehensibility and interest during reading. Journal of the Association for Information Science and Technology, 74(1): 3–16. https://doi.org/10.1002/asi.24657Van der Sluis, F., van den Broek, E. L., Glassey, R. J., van Dijk, E. M. A. G., & de Jong, F. M. G. (2014). When complexity becomes interesting. Journal of the American Society for Information Science and Technology, 65(7): 1478–1500. https://doi.org/10.1002/asi.23095

The benchmark interest rate in Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mexico 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. Mexico 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. Mexico 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 Venezuela was last recorded at 59.23 percent. This dataset provides the latest reported value for - Venezuela Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at -0.215 % pa in 2016. This records a decrease from the previous number of 3.234 % pa for 2015. Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 7.378 % pa from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 18.668 % pa in 1996 and a record low of -0.215 % pa in 2016. Tanzania TZ: 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 Tanzania – Table TZ.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.

The benchmark interest rate in Turkey was last recorded at 43 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.

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.