Graph and download economic data for Underlying Inflation Gauge: Full Data Set Measure (DISCONTINUED) (UIGFULL) from Jan 1995 to Sep 2023 about inflation and USA.

We introduce a survey-based measure of uncertainty about future inflation, asking consumers for density forecasts across inflation outcomes. Consumers are willing and able to express uncertainty, showing high response rates and response patterns that are reliably related to qualitative measures of uncertainty. Heterogeneity in expressed uncertainty is associated with demographic characteristics and financial literacy, and measures of central tendency derived from density forecasts are strongly correlated with point forecasts. Furthermore, expressed uncertainty is positively related to point forecast levels and to larger revisions in point forecasts over time.

A look at the consumer price index for transportation and its components as a measure of inflation faced by consumers.

This dataset has information about the cost of providing General Fund City services per capita of the Full Purpose City population (SD23 measure GTW.A.4). It provides expense information from the annual approved budget document (General Fund Summary and Budget Stabilization Reserve Fund Summary) and population information from the City Demographer's Full Purpose Population numbers. The Consumer Price Index information for Texas is available through the following Key Economic Indicators dataset: https://data.texas.gov/dataset/Key-Economic-Indicators/karz-jr5v. This dataset can be used to help understand the cost of city services over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/ixex-hibp

United States Underlying Inflation Gauge: Full Data Set Measure data was reported at 2.874 % in Sep 2023. This records a decrease from the previous number of 3.032 % for Aug 2023. United States Underlying Inflation Gauge: Full Data Set Measure data is updated monthly, averaging 2.162 % from Jan 1995 (Median) to Sep 2023, with 345 observations. The data reached an all-time high of 6.318 % in Jun 2022 and a record low of -0.648 % in Sep 2009. United States Underlying Inflation Gauge: Full Data Set Measure data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.I: Underlying Inflation Gauge (Discontinued).

About the Dataset

The breakeven inflation rate represents a measure of expected inflation derived from 10-Year Treasury Constant Maturity Securities (BC_10YEAR) and 10-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_10YEAR). The latest value implies what market participants expect inflation to be in the next 10 years, on average. Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department.

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

Inflation rates experienced by different groups of consumers within a country vary. This is because the prices of goods and services and the expenditure patterns of consumers differ. The published inflation rate is used for important decisions regarding the preservation of consumer purchasing power. These include the adjustment of social grants and minimum wages by government and the benchmarking of returns by investors when making investment decisions. It is thus vital that inflation is measured accurately to ensure the purchasing power of consumers is preserved. Current measures of inflation published by Stats SA are applicable to typical consumers and are not relevant to each individual. This resource supplements a study that seeks to provide a publicly available model that can be used by consumers to calculate their personal rate of inflation.

This dataset has information about the cost of providing General Fund City services per capita of the Full Purpose City population (SD23 measure GTW.A.4). It provides expense information from the annual approved budget document (General Fund Summary and Budget Stabilization Reserve Fund Summary) and population information from the City Demographer's Full Purpose Population numbers. The Consumer Price Index information for Texas is available through the following Key Economic Indicators dataset: https://data.texas.gov/dataset/Key-Economic-Indicators/karz-jr5v. This dataset can be used to help understand the cost of city services over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/ixex-hibp

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

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.

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.

We extend time-series models that have so far been used to study price inflation (Stock and Watson 2016) and apply them to a micro-level data set containing worker-level information on hourly wages. We construct a measure of aggregate nominal wage growth that (i) filters out noise and very transitory movements, (ii) quantifies the importance of idiosyncratic factors for aggregate wage dynamics, and (iii) strongly co-moves with labor market tightness, unlike existing indicators of wage inflation. We show that our measure is a reliable real-time indicator of wage pressures and a good predictor of future wage growth.

Japan JP: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at -0.215 % in 2017. This records a decrease from the previous number of 0.274 % for 2016. Japan JP: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging -0.572 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 2.928 % in 1991 and a record low of -1.895 % in 2010. Japan JP: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

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.

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

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Acknowledgements

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

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Much research studies US inflation history with a trend-cycle model with unobserved components, where the trend may be viewed as the Fed's evolving inflation target or long-horizon expected inflation. We provide a novel way to measure the slowly evolving trend and the cycle (or inflation gap), by combining inflation predictions from the Survey of Professional Forecasters (SPF) with realized inflation. The SPF forecasts may be treated either as rational expectations (RE) or updating according to a sticky information (SI) law of motion. We estimate RE and SI state-space models with stochastic volatility on samples of consumer price index and gross national product/gross domestic product deflator inflation and the associated SPF inflation predictions using a particle Metropolis-Markov chain Monte Carlo sampler. The trend converges to 2% and its volatility declines over time-two tendencies largely complete by the late 1990s.

We report average expected inflation rates over the next one through 30 years. Our estimates of expected inflation rates are calculated using a Federal Reserve Bank of Cleveland model that combines financial data and survey-based measures. Released monthly.

This table contains 11 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.