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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.
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.
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Cost of food in the United States increased 2.90 percent in May 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.
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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.
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The IMF has a great inflation database, but it relies on countries to provide their latest data to the IMF, and as such, it can be temporarily out of date. This database will keep the IMF inflation database up to date for African countries by scraping data from individual countries' websites as soon as they release their data and combining it with the latest IMF data. This Africa inflation database powers the ADH Inflation Observer. All 3 datasets found here contain the same data, but in different shapes to suit different applications.
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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.
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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.
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Inflation Rate in Turkey decreased to 35.41 percent in May from 37.86 percent in April 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.
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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.
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Potential output plays a central role in monetary policy and short-term macroeconomic policy making. Yet, characterizing the output gap involves a trend-cycle decomposition, and unobserved component estimates are typically subject to a large uncertainty at the sample end. An important consequence is that output gap estimates can be quite inaccurate in real time, as recently highlighted by Orphanides and van Norden (2002), and this causes a serious problem for policy makers. For the cases of the US, EU-11 and two EU countries, we evaluate the benefits of using inflation data for improving the accuracy of real-time estimates.
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Potential output plays a central role in monetary policy and short-term macroeconomic policy making. Yet, characterizing the output gap involves a trend-cycle decomposition, and unobserved component estimates are typically subject to a large uncertainty at the sample end. An important consequence is that output gap estimates can be quite inaccurate in real time, as recently highlighted by Orphanides and van Norden (2002), and this causes a serious problem for policy makers. For the cases of the US, EU-11 and two EU countries, we evaluate the benefits of using inflation data for improving the accuracy of real-time estimates.
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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.
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<ul style='margin-top:20px;'>
<li>Pakistan inflation rate for 2023 was <strong>30.77%</strong>, a <strong>10.89% increase</strong> from 2022.</li>
<li>Pakistan inflation rate for 2022 was <strong>19.87%</strong>, a <strong>10.38% increase</strong> from 2021.</li>
<li>Pakistan inflation rate for 2021 was <strong>9.50%</strong>, a <strong>0.24% decline</strong> from 2020.</li>
</ul>Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.
Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.
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This paper seeks to address the policy issue of the usefulness of financial spreads as indicators of future inflation and output growth in the countries of the European Union, placing a particular focus on out-of-sample forecasting performance. Such analysis is of considerable relevance to monetary authorities, given the breakdown of the money/income relation in a number of countries and following increased emphasis of domestic monetary policy on control of inflation following the broadening of the ERM bands. The results confirm that for some countries, financial spread variables do contain some information about future output growth and inflation, with the yield curve and the reverse yield gap performing best. However, the relatively poor out-of-sample forecasting performance and/or parameter instability suggests that the need for caution in using spread variables for forecasting in EU countries. Only a small number of spreads contain information, and improve forecasting in a manner which is stable over time.
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Core consumer prices in Egypt increased 13.10 percent in May 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.
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.
The data cover the following sub-national areas: Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Market Average
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Factor models of disaggregate inflation indices suggest that sectoral shocks generate the bulk of sectoral inflation variance, but no persistence. Aggregate shocks, by contrast, are the root of sectoral inflation persistence, but have negligible relative variance. We show that simple factor models do not cope well with essential features of price data. In particular, sectoral inflation series are subject to features such as measurement error, sales and item substitutions. In factor models, these blow up the variance of sector-specific shocks, while reducing their persistence. We control for such effects by estimating a refined factor model and find that inflation variance is driven by both aggregate and sectoral shocks. Sectoral shocks, too, generate substantial inflation persistence. Both findings contrast with earlier evidence from factor models, but align well with recent micro evidence. Our results have implications for the foundations of price stickiness, and provide quantitative inputs for calibrating models with sectoral heterogeneity.
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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.
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The dataset captures the dynamic fluctuations in the exchange rate between the United States Dollar (USD) and the Pakistani Rupee (PKR) over an extensive period, spanning from February 1992 to November 2023. This dataset is a valuable resource for analysts, economists, and researchers seeking to understand the historical trends, patterns, and factors influencing the USD to PKR exchange rate.
Temporal Range: Start Date: February 1992 End Date: November 2023
Frequency: Daily exchange rates
Data Fields: Date: The date for each recorded exchange rate. USD to PKR Exchange Rate: The exchange rate indicating how much one USD is equivalent to in PKR on a given day. Change: Change between last price and today Price
Trend Analysis: Identify long-term trends in the exchange rate over the entire duration. Highlight periods of stability, volatility, or significant fluctuations.
Seasonal Patterns: Explore if there are any recurring seasonal patterns influencing the exchange rate.
Economic Events: Correlate major economic events, both global and domestic, with corresponding shifts in the exchange rate.
Impact of Policies: Assess the impact of monetary and fiscal policies on the USD to PKR exchange rate.
Currency Market Dynamics: Analyze how market dynamics, including demand and supply forces, influence the exchange rate.
Inflation and Interest Rates: Examine the relationship between inflation rates, interest rates, and the exchange rate.
Investment Planning: Assist investors in making informed decisions based on historical exchange rate trends.
Risk Management: Aid businesses in assessing and managing currency risk exposure.
Economic Research: Facilitate research on the impact of economic indicators on exchange rates.
Government Policy Evaluation: Provide insights for policymakers in evaluating the effectiveness of monetary and fiscal policies.
This comprehensive USD to PKR exchange rate dataset offers a wealth of information for various stakeholders. Whether analyzing market trends, conducting economic research, or making strategic financial decisions, this dataset serves as a valuable tool for understanding the historical dynamics of the USD to PKR exchange rate over more than three decades.
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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.