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.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cost of food in the United States increased 2.90 percent in July 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.

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.

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.

Core consumer prices in Egypt increased 11.60 percent in July of 2025 over the same month in the previous year. This dataset provides - Egypt Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Title: Exchange Rate and Inflation Rate on GDP Dataset

Description: This dataset presents a comprehensive collection of historical data on exchange rates, inflation rates, and their impact on Gross Domestic Product (GDP). It serves as a valuable resource for researchers, economists, and data enthusiasts seeking to explore the relationships between these key economic indicators.

The dataset contains information from various countries and regions, covering multiple years, allowing users to analyze how exchange rate fluctuations and inflation rates have influenced the economic performance of different economies over time.

Attributes included in the dataset:

1. Country/Region: The name of the country or region for which the data is recorded.
2. Year: The year to which the data corresponds.
3. Exchange Rate: The exchange rate of the country's currency against a specified reference currency.
4. Inflation Rate: The percentage change in the general price level of goods and services over a specific period.
5. Gross Domestic Product (GDP): The total monetary value of all goods and services produced within a country's borders in a given year.

Use cases: - Analyzing the impact of exchange rate fluctuations on a country's trade balance and economic growth. - Studying the relationship between inflation rates and changes in purchasing power in different economies. - Exploring correlations between GDP growth and exchange rate policies of countries. - Building predictive models to forecast inflation rates or exchange rate movements based on historical data.

Note to users: Please keep in mind that while this dataset provides valuable insights into the relationships between exchange rates, inflation rates, and GDP, it should be used responsibly and in conjunction with other relevant data sources for accurate and robust analyses.

If you have any questions or feedback about this dataset, feel free to reach out. Happy analyzing!

A diverse group of over 30 countries located all over the world—such as the UK, Colombia, and Ghana—introduced inflation targeting, which is a monetary policy that seeks to control inflation through a pre- announced target. Fully institutionalized democracies adopted the policy first because the core features of inflation targeting are con- sistent with the principles of a liberal democracy. But why was in- flation targeting also introduced by less-democratic countries? This article develops the argument that decision makers of less-democratic countries became more likely to adopt inflation targeting when they observed that nearby countries increased the flexibility of the policy. The statistical analysis of data from 76 countries between 1989 and 2013 supports this hypothesis. The finding that the change of a policy toward a more flexible framework drives its global spread addresses a blind spot in the more recent policy diffusion literature.

Inflation Rate in Iran increased to 38.90 percent in April from 37.10 percent in March of 2025. This dataset provides the latest reported value for - Iran 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 Turkey decreased to 33.52 percent in July from 35.05 percent in June 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.

Nigeria’s inflation has been higher than the average for African and Sub-Saharan countries for years now, and even exceeded 16 percent in 2017 – and a real, significant decrease is nowhere in sight. The bigger problem is its unsteadiness, however: An inflation rate that is bouncing all over the place, like this one, is usually a sign of a struggling economy, causing prices to fluctuate, and unemployment and poverty to increase. Nigeria’s economy - a so-called “mixed economy”, which means the market economy is at least in part regulated by the state – is not entirely in bad shape, though. More than half of its GDP is generated by the services sector, namely telecommunications and finances, and the country derives a significant share of its state revenues from oil.

Because it got high

To simplify: When the inflation rate rises, so do prices, and consequently banks raise their interest rates as well to cope and maintain their profit margin. Higher interest rates often cause unemployment to rise. In certain scenarios, rising prices can also mean more panicky spending and consumption among end users, causing debt and poverty. The extreme version of this is called hyperinflation: A rapid increase of prices that is out of control and leads to bankruptcies en masse, devaluation of money and subsequently a currency reform, among other things. But does that mean that low inflation is better? Maybe, but only to a certain degree; the ECB, for example, aspires to maintain an inflation rate of about two percent so as to keep the economy stable. As soon as we reach deflation territory, however, things are starting to look grim again. The best course is a stable inflation rate, to avoid uncertainty and rash actions.

Nigeria today

Nigeria is one of the countries with the largest populations worldwide and also the largest economy in Africa, with its economy growing rapidly after a slump in the aforementioned year 2017. It is slated to be one of the countries with the highest economic growth over the next few decades. Demographic key indicators, like infant mortality rate, fertility rate, and the median age of the population, all point towards a bright future. Additionally, the country seems to make big leaps forward in manufacturing and technological developments, and boasts huge natural resources, including natural gas. All in all, Nigeria and its inflation seem to be on the upswing – or on the path to stabilization, as it were.

ABSTRACTAdministered prices and government discretion. Administered prices during the first term of President Dilma were used as an instrument to meet inflation target, so as to subordinate industrial policies to short run macroeconomic aims. This strategy was ineffective to control inflation and distorted investment and consumption decisions. The article shows that prices tend to deviate more the larger their weight in the price index, and tend to vary consistently with the political cycles. The article concludes with policy suggestions to control the negative effect of deviations of government discretion to determine administered prices.

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

Core consumer prices in the United States increased 3.10 percent in July 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.

The present study investigated whether inhibition of deiodinase, the enzyme which converts thyroxine (T4) to the more biologically-active form, 3,5,3'-triiodothyronine (T3), would impact inflation of the posterior and/or anterior chamber of the swim bladder, processes previously demonstrated to be thyroid-hormone regulated. Two experiments were conducted using a model deiodinase inhibitor, iopanoic acid (IOP). In the first study, fathead minnow (Pimephales promelas) embryos were exposed to 0.6, 1.9, or 6.0 mg IOP/L or control water in a flow-through system until reaching 6 days post-fertilization (dpf) at which time posterior swim bladder inflation was assessed. To examine effects on anterior swim bladder inflation, a second study was conducted with 6 dpf larvae exposed to the same IOP concentrations until reaching 21 dpf. Fish from both studies were sampled for T4/T3 measurements, gene transcription analyses, and thyroid histopathology. In the embryo study, incidence and length of inflated posterior swim bladders were significantly reduced in the 6.0 mg/L treatment at 6 dpf. Incidence of inflation and length of anterior swim bladder in larval fish were significantly reduced in all IOP treatments at 14 dpf, but inflation recovered by 18 dpf. Throughout the larval study, whole body T4 concentrations were significantly increased and T3 concentrations were significantly decreased in all IOP treatments. Consistent with hypothesized compensatory responses, significant up-regulation of deiodinase-2 mRNA was observed in the larval study, and down-regulation of thyroperoxidase mRNA was observed in all IOP treatments in both studies. Taken together, these results support the hypothesized adverse outcome pathways linking inhibition of deiodinase activity to impaired swim bladder inflation. This dataset is associated with the following publication: Cavallin, J., G. Ankley, B. Blackwell, C. Blanksma, K. Fay, K. Jensen, M. Kahl, D. Knapen, P. Kosian, S. Poole, E. Randolph, A. Schroeder, L. Vergauwen, and D. Villeneuve. Impaired swim bladder inflation in early-life stage fathead minnows exposed to a deiodinase inhibitor, iopanoic acid (article). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(11): 2942-2952, (2017).

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

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

This dataset supports the thesis The U.S. Dollar in Crisis: The Role of Asset-Backed Digital Currencies in Its Transformation by Nicolin Decker. It provides empirical data and econometric models to analyze the feasibility of Asset-Backed Digital Currencies (ABDCs) as a stabilizing alternative to fiat monetary systems. Spanning historical macroeconomic data (1970–2024) and projected ABDC circulation trends (2026–2036), the dataset includes inflation-adjusted monetary indicators, crisis response simulations, and global trade impact assessments. Key analyses incorporate Vector Autoregression (VAR), Monte Carlo simulations, Granger causality tests, and DSGE modeling to evaluate ABDC's effect on inflation control, liquidity stability, and financial resilience. The dataset is structured for full reproducibility, ensuring rigorous validation of ABDC’s role in modernizing global monetary policy.

Core consumer prices in Japan increased 3.10 percent in July of 2025 over the same month in the previous year. This dataset provides - Japan Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.