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.

Please, if you use this dataset or do you like my work please UPVOTE 👁️

This dataset provides a comprehensive historical record of inflation rates worldwide, covering the period from 1960 to the present. It includes inflation data at the national level for multiple countries and territories, making it a valuable resource for economic analysis, financial forecasting, and macroeconomic research.

Data Source: https://datos.bancomundial.org/indicador/FP.CPI.TOTL.ZG?end=2023&start=1960&view=chart

Key Features:

✅ Global Coverage – Inflation rates for countries across all continents.

✅ Long-Term Data – Over 60 years of historical records, ideal for trend analysis.

✅ Regional Classification – Data categorized by region, sub-region, and intermediate region for in-depth geographic analysis.

✅ Standardized Indicators – Based on CPI (Consumer Price Index) inflation rates from reputable sources.

Potential Use Cases:

📊 Economic Research – Analyze inflation trends and economic cycles.

📈 Financial Forecasting – Predict future inflation and its impact on global markets.

🌍 Policy & Development Studies – Examine regional disparities and economic policies.

📚 Machine Learning Applications – Train predictive models using historical inflation trends.

This dataset is an essential tool for economists, data scientists, and financial analysts looking to explore global inflation patterns and their implications on economic stability.

Core consumer prices in the United States increased 3 percent in September 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 Expectations in the United States decreased to 3.20 percent in October from 3.40 percent in September of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Inflation Rate in Turkey decreased to 32.87 percent in October from 33.29 percent in September of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus 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.

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.

Content

For further information regarding treasury constant maturity data, please refer to http://www.federalreserve.gov/releases/h15/current/h15.pdf and http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2003-01-02

• Observation End : 2019-12-20

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Fineas Anton on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Replication Files for “Inflation and Trading”

Codes: • 01a_rep_survey_data cleaning.do: cleaning raw survey data • 02a_rep_survey_data prep.do: preparing final survey dataset • 03a_rep_survey_data analysis.do: produces Figures 1-6 and Tables 1-5 and 8 • 02b_rep_bank_data prep.do: preparing final bank dataset • 03b_rep_bank_data analysis.do: produces Tables 6-7

Datasets: The folder 02_data contains survey and bank data. From the survey, we include pseudo data with the same structure as the original data needed to run the do-files 01a, 02a, and 03a, but the dataset contains only a random subsample of 300 respondents with random noise added to each continuous response. The original dataset is not available because it includes confidential information on customers of our partnering bank. • rep_survey_data raw.dta: raw survey data for a random subsample of 300 respondents and with added noise to each continuous variable. We also exclude open-ended responses at the beginning and end of survey for confidentiality reasons. These responses do not feature in the main analysis of the paper • rep_survey_data clean.dta: survey data after transformation of the raw variables • rep_survey_data final.dta: preparation of final survey dataset

From the bank, we include a dataset with the same structure as the original data that allows the do-files 01b and 02b to run. The dataset includes only the necessary variables needed for the analysis, and we select a subsample of customers to match the 300 respondents randomly drawn from the set of survey respondents. The original datasets are not available since they use proprietary information from the partnering bank. • rep_bank_data sum stat pf.dta: portfolio summary statistics, coming from confidential portfolio data from the bank, and used for Table 1 • rep_bank_data sum stat trading: trading summary statistics, coming from tab6a • rep_bank_data tab1.dta: demographics data from bank • rep_bank_data tab6a: trading data from bank • rep_bank_data final.dta: final dataset from bank, which combines tab1, tab6a, and select variables from the survey for the subsample of survey respondents

Runtime: We run the codes on a MacBook Pro laptop with Stata 19 MP. Runtime is below 10 minutes on real data and below one minute on pseudo data.

This dataset is about books. It has 1 row and is filtered where the book is Choice in currency : a way to stop inflation. It features 7 columns including author, publication date, language, and book publisher.

Content

For further information regarding treasury constant maturity data, please refer to http://www.federalreserve.gov/releases/h15/current/h15.pdf and http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2010-02-22

• Observation End : 2019-12-20

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This deposit is a supporting dataset that accompanies the paper "Non-Gaussianity in D3-brane inflation" (arXiv:2105.03637). It contains the primary trajectory catalogue used to generate the statistical results reported in this paper. It also contains a precise specification of the inflationary model (kinetic mixing matrix and potential) in the form of a CppTransport .model file, and supporting files needed to build the COSMOSIS pipeline used to compute observables.

Please note that the .model file is in a non-standard format. If you wish to process it, you will require a specific build of CppTransport (commit hash 35c5ad8f).

This deposit contains the following files:

Main trajectory catalogue

• catalogue.csv. This contains all parameters needed to specify the potential for a particular trajectory. (Initial conditions are not required, because each trajectory begins at the same field-space location. These initial conditions are specified in the COSMOSIS configuration file d3brane_deltamax_38_all_values.ini.) It also contains values for the summary inflationary observables. The columns contained in this file are listed below.

CppTransport setup files

As explained above, the .model file uses a custom format associated with the interface to COSMOSIS. A public release of this interface is planned, but the final .model file format used may change. These files should always be used with CppTransport commit hash 35c5ad8f.

• d3brane_deltamax_38_all.model. CppTransport .model file that fully specifies the D3-brane kinetic mixing matrix and potential.
• christoffel.txt. Contains pre-computed expressions for the components of the connexion. This file is automatically ingested by the CppTransport translator in 35c5ad8f. It should be placed in the same folder as the .model file.
• inv_metric.txt. Contains pre-computed expressions for the inverse field-space metric (kinetic mixing matrix). This file is automatically ingested by the CppTransport translator in 35c5ad8f. It should be placed in the same folder as the .model file.
• riemann.txt. Contains pre-computed expressions for the Riemann tensor associated with the field-space metric. This file is automatically ingested by the CppTransport translator in 35c5ad8f. It should be placed in the same folder as the .model file.

PyTransport setup files

• DBraneSetup.py. PyTransport setup file to install the D3-brane model.

COSMOSIS configuration files

These are used to specify the COSMOSIS pipeline used to compute observables.

• d3brane_deltamax_38_all_mcmc.ini. Main COSMOSIS configuration file specifying the pipeline.
• d3brane_deltamax_38_all_priors.ini. Specifies priors for each sample parameter. Used only during catalogue construction.
• d3brane_deltamax_38_all_values.ini. Specifies fixed parameters of the potential (\(T_3\), \(a_0\), \(\phi_{\mathrm{UV}}\)), cosmological parameters (\(\Omega_c h^2\), \(\Omega_b h^2\), \(h\), \(\tau\)), and field-space initial conditions.

COMOSIS module files

• class_interface.py. A modified version of the CLASS interface to COSMOSIS that accepts a sampled power spectrum (written to a separate file) rather than specifying the power spectrum using \(A_s\), \(n_s\). If the COSMOSIS configuration files are not modified, the pipeline will expect to find this interface in the location

  The other parts of the CLASS module can be copied to this folder. Only class_interface.py needs to be replaced.

Fields included in trajectory catalogue

The main trajectory catalogue catalogue.csv contains a large number of fields.

• trajectory. Unique trajectory label, beginning at 1. Numeric labels used in the corresponding science paper refer to this identified.
• Q, alpha. See Table 1 of the science paper.
• ReBlm_xxx, ImBlm_xxx. Real and imaginary parts of the complex Wilson coefficient for "non-real" zero modes (i.e. modes with at least one of (R), (m_1), (m_2) not zero) appearing in the contributions to the potential from the conifold zero modes. Normalized as in Eq. (2.30) of arXiv v1 of the science paper. Drawn from a Gaussian distribution with mean zero and standard deviation unity. The xxx label is associated with an internal ordering of the modes.
• ReDlm_xxx, ImDlm_xxx. Real and imaginary parts of the complex Wilson coefficient for "non-real" modes appearing in the contributions to the potential from the inhomogeneous term (|\Lambda^2|) in the field equation for (\Phi_-). Normalized as in the discussion below Eq. (2.42) of arXiv v1 of the science paper. Drawn from a Gaussian distribution with mean zero and standard deviation unity. The xxx label is associated with an internal ordering of the modes.
• A_lm. Real Wilson coefficient for real modes (i.e. those with (R = m_1 = m_2 = 0)) appearing in the contributions to the potential from the conifold zero modes. Normalized as in Eq. (2.30) of arXiv v1 of the science paper. Drawn from a Gaussian distribution with mean zero and standard deviation unity. The xxx label is associated with an internal ordering of the modes.
• C_lm. Real Wilson coefficient for real modes appearing in the contributions to the potential from the inhomogeneous term (|\Lambda^2|) in the field equation for (\Phi_-). Normalized as in the discussion below Eq. (2.42) of arXiv v1 of the science paper. Drawn from a Gaussian distribution with mean zero and standard deviation unity. The xxx label is associated with an internal ordering of the modes.
• As, At, r. Values of the power spectra at (k = 0.002 / \mathrm{Mpc}). Defined as in Step 1 in §3.1.1 of arXiv v1 of the science paper.
• ns, nt. Values of the spectral indices (n_s) and (n_t) at (k = 0.002 / \mathrm{Mpc}). Obtained by performing a fit as described in §3.1.2 of arXiv v1 of the science paper. Notice that nt is not used for the tensor spectral index estimates reported in the paper.
• nsfull, ntfull. Fits for the spectral indices using the full wavenumber range provided to CLASS. Has no clear meaning when the spectrum is not accurately fit by a power law over this range of wavenumbers. Used mostly for comparison with ns and nt.
• epsilon, eta. Values for the slow-roll parameters (\epsilon = - \dot{H}/H^2) and (\eta = \mathrm{d}\ln \epsilon / \mathrm{d}N) sampled 60 e-folds before the end of inflation. The value of epsilon is used to estimate (n_t) as described in the science paper.
• kpiv. Wavenumber of the pivot scale (k = 0.002 / \text{Mpc}) in CppTransport internal units normalized so that (k=1) is the mode that exits the horizon 15 e-folds after the initial point.
• Npiv. Horizon-exit time of the pivot scale (k = 0.002/\text{Mpc}) measured from (N=0) at the end of inflation.
• Nefolds. Total number of e-folds attained in this model, measured from (N=0) at the initial point.
• normmassmatrixeigenvalueN-M. Values of (m_N^2 / H^2), where (m_N^2) is the (N^{\text{th}}) ordered eigenvalue of the mass-squared matrix (from light to heavy). (M=1) is 55 e-folds from the end of inflation; (M=2) is 2.5 e-folds from the end of inflation; (M=3) is 1 e-fold from the end of inflation; (M=4) corresponds to the end of inflation.
• Bequi, fNLequi. Bispectrum shape function and reduced bispectrum (respectively) on the equilateral configuration described in Step 2, §3.1.1 of arXiv v1 of the science

Expressed in end of the period, not annual average data. A consumer price index (CPI) measures changes in the prices of goods and services that households consume. Such changes affect the real purchasing power of consumers' incomes and their welfare. As the prices of different goods and services do not all change at the same rate, a price index can only reflect their average movement. A price index is typically assigned a value of unity, or 100, in some reference period and the values of the index for other periods of time are intended to indicate the average proportionate, or percentage, change in prices from this price reference period. Price indices can also be used to measure differences in price levels between different cities, regions or countries at the same point in time. [CPI Manual 2004, Introduction] For euro countries, consumer prices are calculated based on harmonized prices.

Inflation Rate In the Euro Area increased to 2.20 percent in November from 2.10 percent in October 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.

Content

This series measures the probability that the expected personal consumption expenditures price index (PCEPI) inflation rate (12-month percent changes) over the next 12 months will exceed 2.5 percent.

For additional information on the Price Pressures Measure and its construction, see “Introducing the St. Louis Fed Price Pressures Measure” (https://research.stlouisfed.org/publications/economic-synopses/2015/11/06/introducing-the-st-louis-fed-price-pressures-measure/)

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1990-01-01

• Observation End : 2019-11-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Charles on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset provides a comprehensive view of global economic trends, combining multiple essential indicators for analysis and research. The data focuses on the period from 2020 to 2023 and includes two key components:

1. GDP Per Capita and Inflation (2020–2023)

Scope: Yearly GDP per capita (in USD) and inflation rates per countries over the four-year period.

1. Population (2023)

Scope: The total population of each country at the end of 2023.

The dataset is meticulously compiled from trusted sources:

GDP per capita and inflation data are sourced from the World Bank national accounts data and OECD National Accounts data files.

Population data is derived from the World Bank Data Catalog (Population Ranking).

Potential Applications

Analyze the impact of inflation on economic growth during and after the pandemic.

Examine relationships between GDP per capita and population size.

Compare economic indicators across countries and regions.

Key Features: Clean, structured, and ready-to-use format.

Country-level granularity for detailed comparisons.

Suitable for trend analysis, visualizations, and predictive modeling.

Licensing: This dataset is licensed under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, modify, and distribute the data for any purpose, including commercial use, as long as appropriate credit is given to the World Bank.

Content

For further information regarding treasury constant maturity data, please refer to http://www.federalreserve.gov/releases/h15/current/h15.pdf and http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2004-07-27

• Observation End : 2019-12-20

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by NeONBRAND on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Inflation Rate in Australia increased to 3.80 percent in the fourth quarter of 2025 from 3.20 percent in the third quarter of 2025. This dataset provides the latest reported value for - Australia Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Description This dataset is created from different free news apis, consists of 7k+ articles from different countries. The data consists of inflation related news articles from 300+ sources world wide

Start Date : 07-11-2022 End Date : 15-11-2022