Inflation Expectations in the United States increased to 3.20 percent in August from 3.10 percent in July of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in the United States increased to 2.90 percent in August from 2.70 percent in July of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in India increased to 2.07 percent in August from 1.61 percent in July of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Canada increased to 1.90 percent in August from 1.70 percent in July of 2025. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Indonesia decreased to 2.31 percent in August from 2.37 percent in July of 2025. This dataset provides - Indonesia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Unilever's (ULVR) Sustainable Future: Will Growth Outpace Inflation?

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

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

Inflation Rate in Malaysia increased to 1.20 percent in July from 1.10 percent in June of 2025. This dataset provides - Malaysia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The canonical inflation specification in sticky-price rational expectations models (the new-Keynesian Phillips curve) is often criticized for failing to account for the dependence of inflation on its own lags. In response, many studies employ a "hybrid" specification in which inflation depends on its lagged and expected future values, together with a driving variable such as the output gap. We consider some simple tests of the hybrid model that are derived from its closed form. We find that the hybrid model describes inflation dynamics poorly, and find little empirical evidence for the type of rational, forward-looking behavior that the model implies.

ntroducing our retail tariff database, a comprehensive and user-friendly platform designed to provide in-depth information on retail energy tariffs in the GB market. Updated regularly and meticulously maintained, our database offers invaluable insights for a range of stakeholders, including energy retailers, economic analysts, and electric vehicle (EV) operators.

Our retail tariff database covers all types of tariffs available in the GB market and provides an extensive set of data fields, such as tariff types, rates, contract lengths, and more. The platform is designed for easy navigation and customization, allowing users to quickly access the information they need to make informed decisions.

Energy Retailers: For energy retailers, our retail tariff database is an essential tool for staying competitive in the constantly evolving energy market. By providing real-time access to the latest tariffs from competitors, our platform enables retailers to adjust their own pricing strategies and remain competitive in the market. Furthermore, the database offers valuable information on emerging trends and consumer preferences, helping retailers identify new opportunities and challenges in the sector.

Predicting Inflation: For economic analysts and professionals interested in predicting inflation, our retail tariff database serves as a rich source of data for examining the energy market's impact on consumer prices. As energy costs are a significant factor in overall inflation, our platform provides timely and granular information on energy tariffs, allowing users to better understand the relationship between energy prices and inflation. By incorporating this data into their analysis, professionals can develop more accurate predictions and provide valuable insights to policymakers and businesses.

EV Operators: For electric vehicle operators, our retail tariff database offers insights into the evolving landscape of energy pricing, which has a direct impact on the cost and attractiveness of EV charging infrastructure. By staying informed about the latest energy tariffs, EV operators can make strategic decisions regarding the location, pricing, and expansion of their charging networks. Additionally, the database can help operators identify potential synergies between energy tariffs and EV charging demand, enabling them to develop innovative business models that cater to the needs of EV users.

Inflation Rate in Pakistan decreased to 3 percent in August from 4.10 percent in July of 2025. This dataset provides the latest reported value for - Pakistan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These codes help to replicate all the empirical analysis in the article: “What explains monetary policy rate uncertainty? Evidence from the Americas”, Applied Economics Letters (revise and resubmit), authored by Ana Aguilar, Carlos Madeira, Alejandro Parada, Christian Upper (Bank for International Settlements).

The Stata codes use Consensus Economics monthly survey reports with forecasts for countries in the Americas. These forecasts were collected as a Stata dataset, but the files cannot be shared due to copyright concerns. Future users must collect their own Consensus Forecasts data and then use these codes to replicate the empirical analysis of the article.

The data also includes an online appendix with robustness exercises to the main article. These robustness exercises estimate the same uncertainty models, but without the past quarter's inflation rate and GDP growth as additional controls. The results are qualitatively similar to the main article.

Count or zero-inflated count data is often collected in Ecological Momentary Assessment (EMA) studies where understanding the mechanisms of the change process serves as the primary research goal. Traditional methods often fail to capture the complexities inherent in EMA data, leading to discrepancies between empirical findings and theoretical expectations. To address these limitations, this study presents two novel approaches. The first integrates autoregressive effects individually, and the second utilizes a model framework to pinpoint autoregressive effects among groups. These approaches are extended for both count and zero-inflated count data. Through simulation studies and empirical applications, the proposed models show enhanced accuracy and interpretability at both individual and group levels. Furthermore, models tailored for zero-inflated data, referred to as ZIP-CAR, can distinguish zero patterns at both individual and group levels. The dissertation concludes with discussions on the practical implications, limitations, and future directions of the proposed methods. This work is expected to improve method development for EMA studies, ultimately enhancing the understanding of behavioral change processes in zero-inflated count data.

Persistent virus infection can drive CD8+ T-cell responses which are markedly divergent in terms of frequency, phenotype, function, and distribution. On the one hand viruses such as Lymphocytic Choriomeningitis Virus (LCMV) Clone 13 can drive T-cell “exhaustion”, associated with upregulation of checkpoint molecules, loss of effector functions, and diminished control of viral replication. On the other, low-level persistence of viruses such as Cytomegalovirus and Adenoviral vaccines can drive memory “inflation,” associated with sustained populations of CD8+ T-cells over time, with maintained effector functions and a distinct phenotype. Underpinning these divergent memory pools are distinct transcriptional patterns—we aimed to compare these to explore the regulation of CD8+ T-cell memory against persistent viruses at the level of molecular networks and address whether dysregulation of specific modules may account for the phenotype observed. By exploring in parallel and also merging existing datasets derived from different investigators we attempted to develop a combined model of inflation vs. exhaustion and investigate the gene expression networks that are shared in these memory pools. In such comparisons, co-ordination of a critical module of genes driven by Tbx21 is markedly different between the two memory types. These exploratory data highlight both the molecular similarities as well as the differences between inflation and exhaustion and we hypothesize that co-ordinated regulation of a key genetic module may underpin the markedly different resultant functions and phenotypes in vivo—an idea which could be tested directly in future experiments.

This replication package contains the data, code, and instructions to reproduce all results in the paper "Investor Learning about Monetary-Policy Transmission and the Stock Market" by Daniel Andrei and Michael Hasler, forthcoming in the Journal of Financial Economics.

Paper Abstract: We model how investor learning about monetary-policy transmission impacts asset prices. In an asset-pricing model, investors learn from realized inflation surprises how effectively monetary policy steers future inflation. Downward revisions in perceived effectiveness raise expected inflation persistence, increasing return volatility and risk premia. These effects intensify when policy deviates significantly from neutral or monetary-transmission uncertainty is high. We estimate the model using U.S. macro and policy data from 1954 to 2023. The resulting dynamics align with observed patterns in equity returns and volatility. Empirical tests support the model's core prediction: investor learning turns central-bank credibility into a priced risk factor.

This package contains all the data and code necessary to reproduce the figures and tables in the paper and its internet appendix. The data include U.S. macroeconomic time series (real GDP, CPI, Federal funds rate, output gap) from 1954 to 2023, sourced from FRED and NIPA, as well as financial market data. The code includes a Mathematica notebook for solving the theoretical model and other Matlab scripts for the maximum likelihood estimation and all empirical tests. A README file is included with detailed, step-by-step instructions for replication.

Inflation Rate in Spain remained unchanged at 2.70 percent in August. This dataset provides the latest reported value for - Spain Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in South Africa decreased to 3.30 percent in August from 3.50 percent in July of 2025. This dataset provides - South Africa Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Expectations in Australia increased to 4.70 percent in September from 3.90 percent in August of 2025. This dataset provides - Australia Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The EUR/USD exchange rate fell to 1.1797 on September 18, 2025, down 0.24% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 1.34%, and is up by 5.72% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on September of 2025.