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.

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.

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 Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

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 Hong Kong was last recorded at 4.25 percent. This dataset provides the latest reported value for - Hong Kong 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 Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Thailand was last recorded at 1.50 percent. This dataset provides - Thailand Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Russia was last recorded at 16.50 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

Shrinking cities due to low birthrates and aging populations represent a significant urban planning issue. The research question of this study is: which economic, social, and educational factors affect population decline in Japanese shrinking cities? By modeling shrinking cities using the case of Japanese cities, this study aims to clarify the indicators that affect the population change rate. The study employed Bayesian network analysis, a machine learning technique, using a dataset of economic, social, and educational indicators. In conclusion, this study demonstrates that social and educational indicators affect the population decline rate. Surprisingly, the impact of educational indicators is more substantial than that of economic indicators such as the financial strength index. Considering the limitations in fiscal expenditures, increasing investment in education might help solve the problem of shrinking cities because of low birthrates and aging populations. The results provide essential insights and can function as a planning support system.

The benchmark interest rate in Mexico was last recorded at 7.25 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

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

This dataset simulates e-commerce sales data for developing and evaluating forecasting models. It is designed to capture a range of variables that influence online sales in a dynamic and competitive environment. The data includes information on customer behavior, market trends, seasonal patterns, product availability, and other critical factors. By including both internal metrics (e.g., customer engagement, website traffic) and external factors (e.g., economic indicators), the dataset allows for comprehensive analysis and prediction of sales patterns.

Key Features Customer Behavior: A normalized score representing how customers interact with the platform, including engagement levels. Market Trends: Trend indicators representing general market upturns or downturns (e.g., recent demand fluctuations). Seasonal Fluctuations: Categorical data indicating whether sales are affected by high, medium, or low seasonal demand. Product Availability: Count of available units per product, reflecting stock levels. Customer Demographics: Age categories of customers, helping to understand the demographic profile. Website Traffic: Daily visit counts, capturing the volume of potential buyers. Engagement Rate: Percentage of visitors actively interacting with the website, showing engagement quality. Discount Rate: Percentage discount offered on products, affecting customer purchasing behavior. Advertising Spend: Daily spending on digital advertisements, representing marketing efforts. Social Media Engagement: Count of interactions on social media (likes, shares, comments), indicating brand visibility. Returning Customers Rate: Percentage of repeat buyers, showing customer retention. New Customers Count: Number of newly acquired customers per day. Product Categories: The main category of products (e.g., electronics, fashion), enabling analysis by product type. Average Order Value: The average value of orders placed, reflecting purchase amounts. Shipping Speed: Average time for delivery (in days), influencing customer satisfaction. Customer Satisfaction Score: Rating based on customer feedback (1-5 scale). Economic Indicator: Simulated external factor (e.g., consumer confidence) impacting purchasing power. Sales Forecast: Target variable representing the forecasted number of sales.

This paper aims to reveal the changing characteristics of the contribution rates of different production factors in China since the reform and opening up from two dimensions: stage and space. The study used national data from 1978 to 2021 and provincial data from 2000 to 2020, combined with methods such as C-D production function and spatial econometrics for analysis. Research has found that: (1) In terms of stage characteristics, during the structural adjustment stage (1978–1998), economic growth mainly relies on capital and labor input, and the contribution rate of land factors gradually decreases. During the high-speed development stage (1998–2012), the contribution rate of technological factors gradually increased, while the contribution rate of land factors remained relatively stable. In the stage of high-quality development (2012 present), the contribution rate of technological factors continued to rise and become the dominant factor, while the contribution rate of land factors has decreased to a lower level. (2) Regarding spatial characteristics, the spatial econometric model reveals significant spatial agglomeration characteristics of capital and labor, and land factors positively affect local and neighboring economic growth. From 2000 to 2020, the contribution rate of capital factors in various provinces showed a difference of "low in the east and high in the west," which decreased year by year. The contribution rates of labor and land factors have declined to low levels in all provinces; The contribution rate of technological elements has significantly increased, with a higher contribution rate in the southeast region; High-quality development progress in each province can be identified based on the contribution rates of different production factors. The research findings help to understand the impact of varying production factors on economic development at a temporal and spatial scale and provide a scientific basis for achieving a high-quality development pattern of rational allocation of factors and regional coordinated development.

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.

Cumulative variance contribution rate of principal component factors.

This paper aims to reveal the changing characteristics of the contribution rates of different production factors in China since the reform and opening up from two dimensions: stage and space. The study used national data from 1978 to 2021 and provincial data from 2000 to 2020, combined with methods such as C-D production function and spatial econometrics for analysis. Research has found that: (1) In terms of stage characteristics, during the structural adjustment stage (1978–1998), economic growth mainly relies on capital and labor input, and the contribution rate of land factors gradually decreases. During the high-speed development stage (1998–2012), the contribution rate of technological factors gradually increased, while the contribution rate of land factors remained relatively stable. In the stage of high-quality development (2012 present), the contribution rate of technological factors continued to rise and become the dominant factor, while the contribution rate of land factors has decreased to a lower level. (2) Regarding spatial characteristics, the spatial econometric model reveals significant spatial agglomeration characteristics of capital and labor, and land factors positively affect local and neighboring economic growth. From 2000 to 2020, the contribution rate of capital factors in various provinces showed a difference of "low in the east and high in the west," which decreased year by year. The contribution rates of labor and land factors have declined to low levels in all provinces; The contribution rate of technological elements has significantly increased, with a higher contribution rate in the southeast region; High-quality development progress in each province can be identified based on the contribution rates of different production factors. The research findings help to understand the impact of varying production factors on economic development at a temporal and spatial scale and provide a scientific basis for achieving a high-quality development pattern of rational allocation of factors and regional coordinated development.

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