Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Turkey decreased to 35.05 percent in June from 35.41 percent in May 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.

Inflation Expectations in the United States decreased to 3 percent in June from 3.20 percent in May of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar 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.

Core consumer prices in the United States increased 2.80 percent in May 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.

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.

Inflation Rate In the Euro Area increased to 2 percent in June from 1.90 percent in May 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.

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.

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

Inflation Rate in Canada remained unchanged at 1.70 percent in May. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

This table shows inflation, derived inflation and underlying inflation rates. Underlying inflation equals the inflation or derived inflation, excluding certain volatile items or series that are affected by factors other than general economic conditions, for example prices of fuel, vegetables, fruit and government taxes.

Data available from: January 2006 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 16 June 2016: None, this table is stopped.

Changes as of 10 December 2015: On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

The derived CPI decreased by 0.01 index point over August 2015.

Inflation Rate in Vietnam increased to 3.57 percent in June from 3.24 percent in May of 2025. This dataset provides the latest reported value for - Vietnam Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, and required safety net financing. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

Inflation Rate in Germany decreased to 2 percent in June from 2.10 percent in May of 2025. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In addition to the national consumer price index, Statistics Netherlands compiles the harmonised CPI of the Netherlands for the European Union (EU).

Data available from: January 1996 till December 2015

Changes as of 16 June 2016: None, this table is stopped.

Status of the figures: The figures in this table are final.

Changes as of 10 March 2016 Eurostat has changed figures for the Eurozone and the European Union (EU).

Changes as of 10 December 2015 New source material has led to an adjustment of the HICP for The Netherlands in October.

When will new figures be published? Not applicable. This table is succeeded by Consumer prices; price index 2015=100. See paragraph 3.

This table includes figures on the price developments of a package of goods and services purchased by consumers in the Netherlands. The figures are consistent with European directives also known as the harmonised consumer price index (HICP). In all member states of the European Union (EU), these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

This table also contains the HICP at constant taxes: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco).

The table also includes the month-on-month and year-on-year changes of the HICP. The year-on-year change of total consumer expenditure is known as inflation. The figures are shown for 327 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed. The weighting coefficient shows how much consumers in the Netherlands spend on each product group in relation to their total expenditure. The total weighting is 100,000.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, figures are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. The results of the flash estimate are characterized as provisional.

In most cases, the figures are final in the second publication of the same reporting month. Differences between the provisional and final indices are caused by source material that has become available after the flash estimate. The results of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis. In that case, the figures become final one month later.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2000 and 2009, an index was published for the same expenditure category. The base year for that index series was 2005=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

Final figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

Inflation Rate in Egypt decreased to 14.90 percent in June from 16.80 percent in May of 2025. This dataset provides - Egypt Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Fault Lines Widen in the Global Recovery

Economic prospects have diverged further across countries since the April 2021 World Economic Outlook (WEO) forecast. Vaccine access has emerged as the principal fault line along which the global recovery splits into two blocs: those that can look forward to further normalization of activity later this year (almost all advanced economies) and those that will still face resurgent infections and rising COVID death tolls. The recovery, however, is not assured even in countries where infections are currently very low so long as the virus circulates elsewhere.

The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.

Recent price pressures for the most part reflect unusual pandemic-related developments and transitory supply-demand mismatches. Inflation is expected to return to its pre-pandemic ranges in most countries in 2022 once these disturbances work their way through prices, though uncertainty remains high. Elevated inflation is also expected in some emerging market and developing economies, related in part to high food prices. Central banks should generally look through transitory inflation pressures and avoid tightening until there is more clarity on underlying price dynamics. Clear communication from central banks on the outlook for monetary policy will be key to shaping inflation expectations and safeguarding against premature tightening of financial conditions. There is, however, a risk that transitory pressures could become more persistent and central banks may need to take preemptive action.

Risks around the global baseline are to the downside. Slower-than-anticipated vaccine rollout would allow the virus to mutate further. Financial conditions could tighten rapidly, for instance from a reassessment of the monetary policy outlook in advanced economies if inflation expectations increase more rapidly than anticipated. A double hit to emerging market and developing economies from worsening pandemic dynamics and tighter external financial conditions would severely set back their recovery and drag global growth below this outlook’s baseline.

Multilateral action has a vital role to play in diminishing divergences and strengthening global prospects. The immediate priority is to deploy vaccines equitably worldwide. A $50 billion IMF staff proposal, jointly endorsed by the World Health Organization, World Trade Organization, and World Bank, provides clear targets and pragmatic actions at a feasible cost to end the pandemic. Financially constrained economies also need unimpeded access to international liquidity. The proposed $650 billion General Allocation of Special Drawing Rights at the IMF is set to boost reserve assets of all economies and help ease liquidity constraints. Countries also need to redouble collective efforts to reduce greenhouse gas emissions. These multilateral actions can be reinforced by national-level policies tailored to the stage of the crisis that help catalyze a sustainable, inclusive recovery. Concerted, well-directed policies can make the difference between a future of durable recoveries for all economies or one with widening fault lines—as many struggle with the health crisis while a handful see conditions normalize, albeit with the constant threat of renewed flare-ups.