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.

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.

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 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.

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5445802%2F232b3878bd6f687f8337100be97a2059%2F2daa5d51-a570-4240-a994-21b429313d86.webp?generation=1702448669305664&alt=media" alt="">

The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

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

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

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.

Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

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.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Market Average

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.

Monthly and long-term Argentina Inflation data: historical series and analyst forecasts curated by FocusEconomics.