Core consumer prices in the United States increased 2.80 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in India decreased to 2.82 percent in May from 3.16 percent in April of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

This data package includes the underlying data files to replicate the data and charts presented in The Inflation Surge in Europe by Patrick Honohan, PIIE Policy Brief 24-2.

If you use the data, please cite as: Honohan, Patrick. 2024. The Inflation Surge in Europe. PIIE Policy Brief 24-2. Washington, DC: Peterson Institute for International Economics.

Inflation Rate in Venezuela increased to 172 percent in April from 136 percent in March of 2025. This dataset provides - Venezuela Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Comprehensive database of time series covering measures of inflation data for the UK including CPIH, CPI and RPI.

Context

Happiness and well-being are essential indicators of societal progress, often influenced by economic conditions such as GDP and inflation. This dataset combines data from the World Happiness Index (WHI) and inflation metrics to explore the relationship between economic stability and happiness levels across 148 countries from 2015 to 2023. By analyzing key economic indicators alongside social well-being factors, this dataset provides insights into global prosperity trends.

Content

This dataset is provided in CSV format and includes 16 columns, covering both happiness-related features and economic indicators such as GDP per capita, inflation rates, and corruption perception. The main columns include:

Happiness Score & Rank (World Happiness Index ranking per country) Economic Indicators (GDP per capita, inflation metrics) Social Factors (Freedom, Social Support, Generosity) Geographical Information (Country & Continent)

Acknowledgements

The dataset is created using publicly available data from World Happiness Report, Gallup World Poll, and the World Bank. It has been structured for research, machine learning, and policy analysis purposes.

Inspiration

How do economic factors like inflation, GDP, and corruption affect happiness? Can we predict a country's happiness score based on economic conditions? This dataset allows you to analyze these relationships and build models to predict well-being trends worldwide.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Inflation Rate in Nigeria decreased to 22.97 percent in May from 23.71 percent in April of 2025. This dataset provides - Nigeria Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The FAOSTAT monthly Food CPI and General CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB), UNdata, United Nations Conference on Trade and Development (UNCTAD) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI.

The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. Data gaps on monthly Food CPI and General CPI are filled using statistical estimation procedures to have full data coverage for all countries for Food CPI and for General CPI. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The General CPI is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts. The FAOSTAT monthly Food CPI inflation rates are annual year-over-year inflation or percentage change over corresponding month of the previous year.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

Global developments play an important role for domestic inflation rates. Earlier literature has found that a substantial amount of the variation in a large set of national inflation rates can be explained by a single global factor. However, inflation volatility has been typically neglected, although it is clearly relevant both from a policy point of view and for structural analysis and forecasting. We study the evolution of inflation rates in several countries, using a novel model that allows for commonality in both levels and volatilities, in addition to country-specific components. We find that inflation volatility is indeed important, and a substantial fraction of it can be attributed to a global factor that is also driving inflation levels and their persistence. The extent of commonality among core inflation rates and volatilities is substantially smaller than for overall inflation, which leaves scope for national monetary policies. Finally, we show that the point and density forecasting performance of the model is good relative to standard benchmarks, which provides additional evidence on its reliability.

Analysis of ‘Inflation and Unemployment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harisonmwangi/inflation-and-unemployment on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Annual data on inflation and unemployment for countries and regions in the world.

Content

Annual data on inflation and unemployment for countries and regions in the world. Retrieved from World Development Indicators data bank.

Inspiration

The main aim of the data is to explore the relationship between inflation and unemployment rates. Does the Phillips curve in economics still exist, or is it some spurious correlation of data in the '60s?

--- Original source retains full ownership of the source dataset ---

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
• Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Inflation Rate in Russia decreased to 9.40 percent in June from 9.90 percent in May of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.

In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).

In the second section, calculated the variance inflation factor (VIF).

In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).

In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.

Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Factor models of disaggregate inflation indices suggest that sectoral shocks generate the bulk of sectoral inflation variance, but no persistence. Aggregate shocks, by contrast, are the root of sectoral inflation persistence, but have negligible relative variance. We show that simple factor models do not cope well with essential features of price data. In particular, sectoral inflation series are subject to features such as measurement error, sales and item substitutions. In factor models, these blow up the variance of sector-specific shocks, while reducing their persistence. We control for such effects by estimating a refined factor model and find that inflation variance is driven by both aggregate and sectoral shocks. Sectoral shocks, too, generate substantial inflation persistence. Both findings contrast with earlier evidence from factor models, but align well with recent micro evidence. Our results have implications for the foundations of price stickiness, and provide quantitative inputs for calibrating models with sectoral heterogeneity.

Inflation Rate In the Euro Area increased to 2 percent in June from 1.90 percent in May of 2025. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.