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.

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.

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.

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 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.

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.

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

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.

Here 3 DataSet for a complete overview of the economy of the country you are interested in.

Big Mac Index, Inflation forecast and Average Salary

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Big Mac Index

The Big Mac index was invented by The Economist in 1986 as a lighthearted guide to whether currencies are at their “correct” level. It is based on the theory of purchasing-power parity (PPP). By diverting the average national Big Mac prices to U.S. dollars, the same goods can be informally compared. So when the price of a burger is considered, the economic value of all these factors is accounted for. Thus, comparing the prices of similar burgers in two countries reflects a region’s cost of living and affordability. This is the theory behind Burgernomics.

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Inflation forecast

Inflation forecast is measured in terms of the consumer price index (CPI) or harmonised index of consumer prices (HICP) for euro area countries, the euro area aggregate and the United Kingdom. Inflation measures the general evolution of prices. It is defined as the change in the prices of a basket of goods and services that are typically purchased by households. Projections are based on an assessment of the economic climate in individual countries and the world economy, using a combination of model-based analyses and expert judgement. The indicator is expressed in annual growth rates.

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Average Salary (income)

The average salary is calculated based on reported salaries of respondents. The average salary definition is to add the salaries in the sample together, then divide by the number of respondents. The result is the average salary for everyone surveyed.

The dataset is from world data bank and it is from 2020 to 2024 The dataset uses columns as : "country": country which data belong "iso3":short form of country "components":products "currency":currency "start_date_observations" start of observation date "end_date_observations": end of observation date "number_of_markets_modeled":number of market modeled "number_of_markets_covered":number of market covered "number_of_food_items":num of food item in components "number_of_observations_food":num of observation food "number_of_observations_other":observations of others "data_coverage_food"::data coverage of food "data_coverage_previous_12_months_food":for 12 months previous price "total_food_price_increase_since_start_date":total food price "average_annualized_food_inflation":average annualized inflation "maximum_food_drawdown":maximum food drawdown "average_annualized_food_volatility":avg food volatility "average_monthly_food_price_correlation_between_markets":avg monthly food price correlation "average_annual_food_price_correlation_between_markets":annulaly food price correlation "Rsquared_individual_food_items":food item error "Rsquared_individual_other_items":individual item error "index_confidence_score":confidence score "imputation_model":principle used

data source:https://microdata.worldbank.org/index.php/catalog/6160

STUDY TYPE Monthly currency exchange rate estimates in fragile countries

SERIES INFORMATION Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.

RTP consists of three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.

RTFP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP_v02_M RTEP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTEP_v01_M RTFX: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTFX_v01_M To produce smooth price series, outliers in the data are often adjusted using non-parametric density estimation and other techniques. Generalized Auto-Regressive Conditional Heteroskedasticity models are used to estimate intra-month price ranges. These models allow for excess kurtosis using a Generalized Error Distribution (GED). Open, High, Low, and Close price estimates are provided based on the modeled time-varying price distributions.

Data are produced from 2007 to the present and estimates are given for individual commodity items at geo-referenced market locations. Predicted data for missing entries are based on exchange rates, and price data available either at other market locations or from related price items.

RTP estimates of historical and current prices may serve as proxies for sub-national price inflation series or substitute national-level Consumer Price Inflation (CPI) indicators when complete information is unavailable. Therefore, RTP data may differ from other sources with official data, including the World Bank’s International Comparison Program (ICP) or inflation series reported in the World Development Indicators.

Review of Economics and Statistics: Forthcoming. Visit https://dataone.org/datasets/sha256%3A484a7528969ca82fb4a3ebd26409aa9adc1186c67c1138dbce78e92f7b053b40 for complete metadata about this dataset.

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.

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.

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 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.

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

Replication data for publication. Visit https://dataone.org/datasets/sha256%3A68b130dccea64b8f51a75c2f74dc2dda12809edfba2ff302762b9f3d4b5c1bc3 for complete metadata about this dataset.

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.

Inflation Rate in Romania decreased to 9.80 percent in October from 9.90 percent in September of 2025. This dataset provides - Romania Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.