We provide explanations of basic and fundamental concepts on the definition of inflation, measurement of inflation, costs of inflation, the importance of measuring and controlling inflation, the role of the Federal Reserve in inflation, and other concepts such as price indexes, hyperinflation, trend and underlying inflation, measures of inflation like CPI, core CPI, median CPI, trimmed-mean CPI, PCE, core PCE, and trimmed-mean PCE.

This dataset provides detailed Consumer Price Index (CPI) data to support economic research, financial forecasting, and market analysis of Food Items in the United States of America from the years 2002 to 2023. The CPI is a crucial economic indicator that measures the average change over time in the prices paid by consumers for goods and services. This dataset is ideal for analyzing inflation trends, assessing purchasing power, and understanding market behavior.

Learn the differences between the consumer price index (CPI) and the personal consumption expenditures (PCE) price index. Find out what measures are used to gauge underlying inflation, or the long-term trend in prices, such as median and trimmed-mean inflation rates and core inflation.

This dataset provides a comprehensive collection of monthly U.S. macroeconomic indicators spanning January 2000 to December 2024.

It was designed specifically for machine learning-based inflation forecasting and includes key economic factors historically associated with inflation trends:

• Consumer Price Index (CPI) & Inflation Rate
• Unemployment Rate
• Federal Funds Rate
• M2 Money Supply
• Crude Oil Prices (WTI)
• Producer Price Index (PPI)

Primary Goal: Build predictive models to forecast year-over-year inflation rates

Possible Use Cases:

• Forecasting inflation using machine learning models like XGBoost, Random Forest, or LSTM.
• Studying relationships between macroeconomic factors and inflationary pressure.
• Comparing classical econometric approaches with modern AI-based forecasting techniques.

Structure: Each CSV contains a Date column and corresponding metric values, making it easy to merge and align data for analysis.

License: MIT License – free to use for research and educational purposes.

This data is used for examination of inflation- unemployment relationship for 18 countries after 1991. Inflation data is obtained from World Bank database (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG) and unemployment data is obtained from International Labor Organization (http://www.ilo.org/wesodata/).

Analysis period is different for all countries because of structural breaks determined by single point change point detection algorithm included in changepoint package of Killick & Eckley (2014). Granger-causality is conducted with Toda&Yamamoto (1995) procedure. Integration levels are determined with 3 stationary tests. VAR models are run with vars package (Pfaff, Stigler & Pfaff; 2018) without trend and constant terms. Cointegration test is conducted with urca package (Pfaff, Zivot, Stigler & Pfaff; 2016).

All data files are .csv files. Analyst need to change country index (variable name: j) in order to see individual results. Findings can be seen in the article.

Killick, R., & Eckley, I. (2014). changepoint: An R package for changepoint analysis. Journal of statistical software, 58(3), 1-19.

Pfaff, B., Stigler, M., & Pfaff, M. B. (2018). Package ‘vars’. Online] https://cran. r-project. org/web/packages/vars/vars. pdf.

Pfaff, B., Zivot, E., Stigler, M., & Pfaff, M. B. (2016). Package ‘urca’. Unit root and cointegration tests for time series data. R package version, 1-2.

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of econometrics, 66(1-2), 225-250.

We provide explanations of basic and fundamental concepts on the definition of inflation, measurement of inflation, costs of inflation, the importance of measuring and controlling inflation, the role of the Federal Reserve in inflation, and other concepts such as price indexes, hyperinflation, trend and underlying inflation, measures of inflation like CPI, core CPI, median CPI, trimmed-mean CPI, PCE, core PCE, and trimmed-mean PCE.

This dataset contains economic indicators for Pakistan spanning from 1986 to 2006 (21 years of data). Here's what the dataset includes: Dataset Overview:

Time Period: 1986-2006 Country: Pakistan Purpose: Inflation forecasting analysis

Variables/Columns:

Year - Time period identifier Inflation - Inflation rates (ranging from about 2.9% to 12.4%) [Column C] - Unlabeled column with values like 16.65, 17.4, 18, etc. GDP Growth - Economic growth rates (ranging from 1% to 7.8%) Unemployment - Unemployment rates (mostly between 3-8%) Broad Money - Monetary supply indicator (values in hundreds) Exports - Export values Imports - Import values Oil rents - Oil-related economic indicator (mostly below 1.0) Remittances - Foreign remittance values

Key Characteristics:

Comprehensive macroeconomic dataset Covers multiple economic indicators that typically influence inflation Suitable for econometric analysis and forecasting models Includes both monetary (broad money, remittances) and real sector variables (GDP, unemployment) Trade variables (exports/imports) for external sector analysis

This appears to be a well-structured dataset for studying inflation dynamics and building forecasting models for Pakistan's economy.

Since the COVID-19 pandemic, the United States has experienced sharply rising then falling inflation alongside persistent labor market imbalances. This Economic Commentary interprets these macroeconomic dynamics, as represented by the Beveridge and Phillips curves, through the lens of a macroeconomic model. It uses the structure of the model to rationalize the debate about whether the US economy can expect a hard or soft landing. The model is surprised by the resiliency of the labor market as the US economy experienced disinflation. We suggest that the model’s limited ability to capture this resiliency is a feature of using a linear model to forecast the historically unprecedented movements seen after the pandemic among inflation, unemployment, and vacancy rates. We explain how, by adjusting the model to mimic congestion in a tight labor market and greater wage and price flexibility in a high-inflation environment, as during the post-pandemic period, the model can then capture what has been a path consistent with a soft landing.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Take a look at $1 dollar value in 1701 when adjusted for inflation.

Acknowledgements

Data source: https://www.in2013dollars.com/

This dataset holds valuable insights that can be leveraged by researchers, analysts, and policymakers to better understand the complex interactions between financial markets and food price inflation. Here are some potential insights that users could gain from this dataset:

Market-Food Price Correlation: By examining the relationship between financial market data (Open, High, Low, Close) and food price inflation, users can identify potential correlations. For example, they may uncover patterns where food price inflation impacts market sentiment or vice versa.

Inflation rates for the lowest income households were almost always higher than for the highest income households between 2005 and 2021. The biggest difference was seen in December 2008, when the lowest income households experienced inflation rates 0.8 percent greater than the highest income households. In 2021, the difference in the inflation rate experienced by the lowest income households and the highest income households fell considerably, reaching -0.52 percent in July 2021, meaning that inflation was 0.52 percent higher for the highest earners versus the lowest earners.

The Consumer Price Index The consumer price index (CPI) measures the rate of inflation on a basket of goods as a way to document the general inflationary experience of all urban consumers. While this measure of inflation can give us insights into the general price increases of consumer goods, it may not reflect the actual inflation experienced by any given household. Consumers from different income brackets actually behave quite differently when it comes to consumption preferences and their willingness to pay.

Inflation in 2022 2022 was an exceptional year for inflation worldwide due to a multitude of factors relating to the COVID-19 pandemic and the Russian invasion of Ukraine. The inflation rate in the United States reached a high of 9.1 percent during the summer, with consumers experiencing record fuel prices, and increased concerns over the state of the economy. Despite the 2021 figures indicating that inflation has been higher for the highest earners, the pandemic saw U.S. billionaires increase their wealth by 57 percent between March 2020 and March 2022.

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.

Inflation: Friend or Foe to the Stock Market?

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

Empirical analyses of Cagan’s money demand schedule for hyper-inflation have largely ignored the explosive nature of hyper-inflationary data. It is argued that this contributes to an (i) inability to model the data to the end of the hyper-inflation, and to (ii) discrepancies between “estimated” and “actual” inflation tax. Using data from the extreme Yugoslavian hyper-inflation it is shown that a linear analysis of levels of prices and money fails in addressing these issues even when the explosiveness is taken into account. The explanation is that log real money has random walk behaviour while the growth of log prices is explosive. A simple solution to these issues is found by replacing the conventional measure of inflation by the cost of holding money.

Inflation Rate in Japan increased to 3 percent in October from 2.90 percent in September of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ABSTRACT The main purpose of this work is to conduct a systematic literature review regarding inflation expectations, their determinants, and their implications for policy making in Latin America. The analysis shows the importance of inflation expectations in the countries that use an inflation targeting scheme, while also supporting the idea that inflation expectations can affect other sectors of the economy. As for the determinants of expectations, the findings show the importance of past iterations of expectations, supporting the idea that the inflation expectations are heavily determined by themselves. The amount of research being conducted in this field is not comprehensive. This is even more evident in the Latin American region since it is a recent research field with a meager number of publications, deeming our study useful for future research. The classification process makes it easier to know the most common variables and econometric methods used to find the determinants of inflation expectations and their impact on other economic variables.

Empirical studies document a decline in inflation persistence in the 1980s, around the time of the Volcker disinflation. The most common explanation for this decline is a more aggressive response of monetary policy to inflation. We propose an alternative explanation: inflation persistence fell due to the lower trend inflation rate the Volcker disinflation produced. Our explanation suggests higher inflation persistence could be an important consideration in the debate about the costs and benefits of a higher inflation target.

An I(2) analysis of Australian inflation and the markup is undertaken within an imperfect competition model. It is found that the levels of prices and costs are best characterized as integrated of order 2 and that a linear combination of the levels (which may be defined as the markup) cointegrates with price inflation. From the empirical analysis we obtain a long-run relationship where higher inflation is associated with a lower markup and vice versa. The impact in the long run of inflation on the markup is interpreted as the cost to firms of overcoming missing information when adjusting prices in an inflationary environment.

In March 2025, inflation amounted to 2.4 percent, while wages grew by 4.3 percent. The inflation rate has not exceeded the rate of wage growth since January 2023. Inflation in 2022 The high rates of inflation in 2022 meant that the real terms value of American wages took a hit. Many Americans report feelings of concern over the economy and a worsening of their financial situation. The inflation situation in the United States is one that was experienced globally in 2022, mainly due to COVID-19 related supply chain constraints and disruption due to the Russian invasion of Ukraine. The monthly inflation rate for the U.S. reached a 40-year high in June 2022 at 9.1 percent, and annual inflation for 2022 reached eight percent. Without appropriate wage increases, Americans will continue to see a decline in their purchasing power. Wages in the U.S. Despite the level of wage growth reaching 6.7 percent in the summer of 2022, it has not been enough to curb the impact of even higher inflation rates. The federally mandated minimum wage in the United States has not increased since 2009, meaning that individuals working minimum wage jobs have taken a real terms pay cut for the last twelve years. There are discrepancies between states - the minimum wage in California can be as high as 15.50 U.S. dollars per hour, while a business in Oklahoma may be as low as two U.S. dollars per hour. However, even the higher wage rates in states like California and Washington may be lacking - one analysis found that if minimum wage had kept up with productivity, the minimum hourly wage in the U.S. should have been 22.88 dollars per hour in 2021. Additionally, the impact of decreased purchasing power due to inflation will impact different parts of society in different ways with stark contrast in average wages due to both gender and race.

Graph and download economic data for Personal Consumption Expenditures: Chain-type Price Index (PCEPI) from Jan 1959 to Aug 2025 about chained, headline figure, PCE, consumption expenditures, consumption, personal, inflation, price index, indexes, price, and USA.