100+ datasets found
  1. kenya inflation - Dataset - ADH Data Portal

    • ckan.africadatahub.org
    Updated Sep 10, 2021
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    ckan.africadatahub.org (2021). kenya inflation - Dataset - ADH Data Portal [Dataset]. https://ckan.africadatahub.org/dataset/kenya-inflation
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    Dataset updated
    Sep 10, 2021
    Dataset provided by
    CKANhttps://ckan.org/
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    Kenya
    Description

    (i) 12-month inflation: normally considered as inflation rate, is defined as the percentage change in the monthly consumer price index (CPI). For example, the 12-month inflation rate for November 2017 is the percentage change in the CPI of November 2017 and November 2016. (ii) Annual average inflation: is the percentage change in the annual average consumer price index (CPI) of the corresponding months e.g. November 2017 and November 2016. (iii) Source: Kenya National Bureau of Statistics. The latest Kenyan Inflation rates can be downloaded here

  2. Consumer Price Data and Measures Explained

    • clevelandfed.org
    csv
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    Federal Reserve Bank of Cleveland, Consumer Price Data and Measures Explained [Dataset]. https://www.clevelandfed.org/center-for-inflation-research/consumer-price-data
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    csvAvailable download formats
    Dataset authored and provided by
    Federal Reserve Bank of Clevelandhttps://www.clevelandfed.org/
    Description

    We explain how measures of consumer prices are computed and what the differences are between the consumer price index (CPI) and the personal consumption expenditures (PCE) price index. We also explain various measures used to gauge underlying inflation, or the long-term trend in prices, such as median and trimmed-mean inflation rates and core inflation.

  3. J

    Output and inflation in the long run (replication data)

    • journaldata.zbw.eu
    • jda-test.zbw.eu
    txt, zip
    Updated Dec 8, 2022
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    Neil R. Ericsson; John S. Irons; Ralph W. Tryon; Neil R. Ericsson; John S. Irons; Ralph W. Tryon (2022). Output and inflation in the long run (replication data) [Dataset]. http://doi.org/10.15456/jae.2022314.0708044477
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    txt(14551), zip(2235580)Available download formats
    Dataset updated
    Dec 8, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Neil R. Ericsson; John S. Irons; Ralph W. Tryon; Neil R. Ericsson; John S. Irons; Ralph W. Tryon
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cross-country regressions explaining output growth often obtain a negative effect from inflation. However, that result is not robust, due to the selection of countries in sample, temporal aggregation, and omission of consequential variables in levels. This paper demonstrates some implications of these mis-specifications, both analytically and empirically. In particular, for most G-7 countries, annual time series of inflation and the log-level of output are cointegrated, thus rejecting the existence of a long-run relation between output growth and inflation. Typically, output and inflation are positively related in these cointegrating relationships: a price markup model helps to interpret this surprising feature.

  4. o

    Replication data for: Can Rational Expectations Sticky-Price Models Explain...

    • openicpsr.org
    • datasearch.gesis.org
    Updated Dec 6, 2019
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    Jeremy Rudd; Karl Whelan (2019). Replication data for: Can Rational Expectations Sticky-Price Models Explain Inflation Dynamics? [Dataset]. http://doi.org/10.3886/E116078V1
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    Dataset updated
    Dec 6, 2019
    Dataset provided by
    American Economic Association
    Authors
    Jeremy Rudd; Karl Whelan
    Description

    The canonical inflation specification in sticky-price rational expectations models (the new-Keynesian Phillips curve) is often criticized for failing to account for the dependence of inflation on its own lags. In response, many studies employ a "hybrid" specification in which inflation depends on its lagged and expected future values, together with a driving variable such as the output gap. We consider some simple tests of the hybrid model that are derived from its closed form. We find that the hybrid model describes inflation dynamics poorly, and find little empirical evidence for the type of rational, forward-looking behavior that the model implies.

  5. T

    Nigeria Inflation Rate

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 15, 2025
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    TRADING ECONOMICS (2025). Nigeria Inflation Rate [Dataset]. https://tradingeconomics.com/nigeria/inflation-cpi
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    xml, excel, json, csvAvailable download formats
    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1996 - Apr 30, 2025
    Area covered
    Nigeria
    Description

    Inflation Rate in Nigeria decreased to 23.71 percent in April from 24.23 percent in March of 2025. This dataset provides - Nigeria Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  6. U.S. projected annual inflation rate 2010-2029

    • statista.com
    Updated Aug 21, 2024
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    Statista (2024). U.S. projected annual inflation rate 2010-2029 [Dataset]. https://www.statista.com/statistics/244983/projected-inflation-rate-in-the-united-states/
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    Dataset updated
    Aug 21, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

  7. J

    An I(2) analysis of inflation and the markup (replication data)

    • journaldata.zbw.eu
    • jda-test.zbw.eu
    .dat, txt, xls
    Updated Dec 8, 2022
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    Anindya Banerjee; Lynne Cockerell; Bill Russell; Anindya Banerjee; Lynne Cockerell; Bill Russell (2022). An I(2) analysis of inflation and the markup (replication data) [Dataset]. http://doi.org/10.15456/jae.2022314.0708615730
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    .dat(9327), xls(34816), txt(2708)Available download formats
    Dataset updated
    Dec 8, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Anindya Banerjee; Lynne Cockerell; Bill Russell; Anindya Banerjee; Lynne Cockerell; Bill Russell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    An I(2) analysis of Australian inflation and the markup is undertaken within an imperfect competition model. It is found that the levels of prices and costs are best characterized as integrated of order 2 and that a linear combination of the levels (which may be defined as the markup) cointegrates with price inflation. From the empirical analysis we obtain a long-run relationship where higher inflation is associated with a lower markup and vice versa. The impact in the long run of inflation on the markup is interpreted as the cost to firms of overcoming missing information when adjusting prices in an inflationary environment.

  8. T

    Germany Inflation Rate

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 30, 2025
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    TRADING ECONOMICS (2025). Germany Inflation Rate [Dataset]. https://tradingeconomics.com/germany/inflation-cpi
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    json, csv, excel, xmlAvailable download formats
    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1950 - May 31, 2025
    Area covered
    Germany
    Description

    Inflation Rate in Germany remained unchanged at 2.10 percent in May. 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.

  9. U.S. monthly inflation rate 2025

    • statista.com
    • ai-chatbox.pro
    Updated Mar 11, 2025
    + more versions
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    Statista (2025). U.S. monthly inflation rate 2025 [Dataset]. https://www.statista.com/statistics/273418/unadjusted-monthly-inflation-rate-in-the-us/
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    Dataset updated
    Mar 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2021 - Jan 2025
    Area covered
    United States
    Description

    In January 2025, prices had increased by three percent compared to January 2024 according to the 12-month percentage change in the consumer price index — the monthly inflation rate for goods and services in the United States. The data represents U.S. city averages. In economics, the inflation rate is a measure of the change in price level over time. The rate of decrease in the purchasing power of money is approximately equal. A projection of the annual U.S. inflation rate can be accessed here and the actual annual inflation rate since 1990 can be accessed here. InflationOne of the most important economic indicators is the development of the Consumer Price Index in a country. The change in this price level of goods and services is defined as the rate of inflation. The inflationary situation in the United States had been relatively severe in 2022 due to global events relating to COVID-19, supply chain restrains, and the Russian invasion of Ukraine. More information on U.S. inflation may be found on our dedicated topic page. The annual inflation rate in the United States has increased from 3.2 percent in 2011 to 8.3 percent in 2022. This means that the purchasing power of the U.S. dollar has weakened in recent years. The purchasing power is the extent to which a person has available funds to make purchases. According to the data published by the International Monetary Fund, the U.S. Consumer Price Index (CPI) was about 258.84 in 2020 and is forecasted to grow up to 325.6 by 2027, compared to the base period from 1982 to 1984. The monthly percentage change in the Consumer Price Index (CPI) for urban consumers in the United States was 0.1 percent in March 2023 compared to the previous month. In 2022, countries all around the world are experienced high levels of inflation. Although Brazil already had an inflation rate of 8.3 percent in 2021, compared to the previous year, while the inflation rate in China stood at 0.85 percent.

  10. J

    Commodity prices and inflation risk (replication data)

    • jda-test.zbw.eu
    • journaldata.zbw.eu
    csv, txt
    Updated Jul 22, 2024
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    Anthony Garratt; Ivan Petrella; Anthony Garratt; Ivan Petrella (2024). Commodity prices and inflation risk (replication data) [Dataset]. https://jda-test.zbw.eu/dataset/commodity-prices-and-inflation-risk
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    csv(107794), csv(8810), csv(4873), csv(110118), txt(3627), csv(6510), csv(4523), csv(9159), csv(109621)Available download formats
    Dataset updated
    Jul 22, 2024
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Anthony Garratt; Ivan Petrella; Anthony Garratt; Ivan Petrella
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in-sample and out-of-sample predictive ability from commodity prices and convenience yields to inflation, establishing clear point and density forecast performance gains when incorporating disaggregated commodities price information. The resulting forecast densities are used to calculate the (ex-ante) risk of inflation breaching defined thresholds that broadly characterize periods of high and low inflation. We find that information in commodity prices significantly enhances our ability to pick out tail inflation events and to characterize the level of risks associated with periods of high volatility in commodity prices.

  11. F

    Inflation, consumer prices for the United States

    • fred.stlouisfed.org
    json
    Updated Apr 16, 2025
    + more versions
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    (2025). Inflation, consumer prices for the United States [Dataset]. https://fred.stlouisfed.org/series/FPCPITOTLZGUSA
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    jsonAvailable download formats
    Dataset updated
    Apr 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    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.

  12. T

    Japan Inflation Rate

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Mar 20, 2025
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    TRADING ECONOMICS (2025). Japan Inflation Rate [Dataset]. https://tradingeconomics.com/japan/inflation-cpi
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    csv, json, excel, xmlAvailable download formats
    Dataset updated
    Mar 20, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1958 - Apr 30, 2025
    Area covered
    Japan
    Description

    Inflation Rate in Japan remained unchanged at 3.60 percent in April. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  13. Global inflation rate from 2000 to 2029

    • statista.com
    • ai-chatbox.pro
    Updated Jan 10, 2025
    + more versions
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    Statista (2025). Global inflation rate from 2000 to 2029 [Dataset]. https://www.statista.com/statistics/256598/global-inflation-rate-compared-to-previous-year/
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    Dataset updated
    Jan 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2024
    Area covered
    Worldwide
    Description

    Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

  14. F

    5-Year Breakeven Inflation Rate

    • fred.stlouisfed.org
    json
    Updated Jun 6, 2025
    + more versions
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    (2025). 5-Year Breakeven Inflation Rate [Dataset]. https://fred.stlouisfed.org/series/T5YIE
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    jsonAvailable download formats
    Dataset updated
    Jun 6, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Description

    Graph and download economic data for 5-Year Breakeven Inflation Rate (T5YIE) from 2003-01-02 to 2025-06-06 about spread, interest rate, interest, 5-year, inflation, rate, and USA.

  15. Historical United States Money Growth, Inflation, and Inflation Credibility

    • icpsr.umich.edu
    Updated Jun 23, 1999
    + more versions
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    Dewald, William G. (1999). Historical United States Money Growth, Inflation, and Inflation Credibility [Dataset]. http://doi.org/10.3886/ICPSR01198.v1
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    Dataset updated
    Jun 23, 1999
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Dewald, William G.
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/1198/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/1198/terms

    Area covered
    United States
    Description

    This research focuses on the longer-term monetary relationships in historical data. Charts describing the 10-year average growth rates in the M2 monetary aggregate, nominal GDP, real GDP, and inflation are used to show that there is a consistent longer-term correlation between M2 growth, nominal GDP growth, and inflation but not between such nominal variables and real GDP growth. The data reveal extremely long cycles in monetary growth and inflation, the most recent of which was the strong upward trend in M2 growth, nominal GDP growth, and inflation during the 1960s and 1970s, and the strong downward trend since then. Data going back to the 19th century show that the most recent inflation/disinflation cycle is a repetition of earlier long monetary growth and inflation cycles in the United States historical record. Also discussed is a measure of bond market inflation credibility, defined as the difference between averages in long-term bond rates and real GDP growth. By this measure, inflation credibility hovered close to zero during the 1950s and early 1960s, but then rose to a peak of about 10 percent in the early 1980s. During the 1990s, the bond market has yet to restore the low inflation credibility that existed before inflation turned up during the 1960s. The conclusion is that the risks of starting another costly inflation/disinflation cycle could be avoided by monitoring monetary growth and maintaining a sufficiently tight policy to keep inflation low. An environment of credible price stability would allow the economy to function unfettered by inflationary distortions, which is all that can reasonably be expected of monetary policy, and is precisely what should be expected.

  16. U.S. 12-month percentage of change CPI-U 2025, by expenditure category

    • statista.com
    Updated Mar 11, 2025
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    Statista (2025). U.S. 12-month percentage of change CPI-U 2025, by expenditure category [Dataset]. https://www.statista.com/statistics/216055/annual-percentage-of-change-in-the-us-cpi-u-by-expenditure-category/
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    Dataset updated
    Mar 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    United States
    Description

    In January 2025, gasoline prices were around 0.2 percent lower than in January 2024. The data represents city averages in the United States. The defined base period is: 1982-84=100. CPI is defined by the United States Bureau of Labor Statistics as "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services". It is based on prices of food, clothing, shelter, fuels, transportation fares, charges for doctors’ and dentists’ services, drugs, and other goods and services that people buy for day-to-day living. The annual inflation rate in the U.S. since 1990 can be accessed here.

  17. T

    Vietnam Inflation Rate

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 6, 2025
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    TRADING ECONOMICS (2025). Vietnam Inflation Rate [Dataset]. https://tradingeconomics.com/vietnam/inflation-cpi
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset updated
    Jun 6, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1996 - May 31, 2025
    Area covered
    Vietnam
    Description

    Inflation Rate in Vietnam increased to 3.24 percent in May from 3.12 percent in April 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.

  18. d

    Replication data for: Job-to-Job Mobility and Inflation

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Faccini, Renato; Melosi, Leonardo (2023). Replication data for: Job-to-Job Mobility and Inflation [Dataset]. http://doi.org/10.7910/DVN/SMQFGS
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Faccini, Renato; Melosi, Leonardo
    Description

    Replication files for "Job-to-Job Mobility and Inflation" Authors: Renato Faccini and Leonardo Melosi Review of Economics and Statistics Date: February 2, 2023 -------------------------------------------------------------------------------------------- ORDERS OF TOPICS .Section 1. We explain the code to replicate all the figures in the paper (except Figure 6) .Section 2. We explain how Figure 6 is constructed .Section 3. We explain how the data are constructed SECTION 1 Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results. Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics: -------------------------------------------------------------------------------------------- Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz Installed RAM 64.0 GB System type 64-bit operating system, x64-based processor -------------------------------------------------------------------------------------------- It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test: -------------------------------------------------------------------------------------------- MATLAB Version: 9.7.0.1190202 (R2019b) MATLAB License Number: 363305 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode -------------------------------------------------------------------------------------------- MATLAB Version 9.7 (R2019b) Financial Toolbox Version 5.14 (R2019b) Optimization Toolbox Version 8.4 (R2019b) Statistics and Machine Learning Toolbox Version 11.6 (R2019b) Symbolic Math Toolbox Version 8.4 (R2019b) -------------------------------------------------------------------------------------------- The replication code uses auxiliary files and save the pictures in various subfolders: \JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below. \gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano. \filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample. \SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically. \NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab. \NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions. \Graphics: It contains useful codes enabling features to construct some of the graphs in the paper. \Data: it contains the data set used in the paper. \Params: It contains a spreadsheet with the values attributes to the model parameters. \VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2. The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures SECTION 2 The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the average of earnings growth across the previous four months. Growth rates are annualized quarterly values. The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. As in Panel A, expected wage inflation is given by the... Visit https://dataone.org/datasets/sha256%3A44c88fe82380bfff217866cac93f85483766eb9364f66cfa03f1ebdaa0408335 for complete metadata about this dataset.

  19. g

    Historical United States Money Growth, Inflation, and Inflation Credibility...

    • search.gesis.org
    Updated Feb 26, 2021
    + more versions
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    Dewald, William G. (2021). Historical United States Money Growth, Inflation, and Inflation Credibility - Version 1 [Dataset]. http://doi.org/10.3886/ICPSR01198.v1
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    Dataset updated
    Feb 26, 2021
    Dataset provided by
    GESIS search
    ICPSR - Interuniversity Consortium for Political and Social Research
    Authors
    Dewald, William G.
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de433775https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de433775

    Area covered
    United States
    Description

    Abstract (en): This research focuses on the longer-term monetary relationships in historical data. Charts describing the 10-year average growth rates in the M2 monetary aggregate, nominal GDP, real GDP, and inflation are used to show that there is a consistent longer-term correlation between M2 growth, nominal GDP growth, and inflation but not between such nominal variables and real GDP growth. The data reveal extremely long cycles in monetary growth and inflation, the most recent of which was the strong upward trend in M2 growth, nominal GDP growth, and inflation during the 1960s and 1970s, and the strong downward trend since then. Data going back to the 19th century show that the most recent inflation/disinflation cycle is a repetition of earlier long monetary growth and inflation cycles in the United States historical record. Also discussed is a measure of bond market inflation credibility, defined as the difference between averages in long-term bond rates and real GDP growth. By this measure, inflation credibility hovered close to zero during the 1950s and early 1960s, but then rose to a peak of about 10 percent in the early 1980s. During the 1990s, the bond market has yet to restore the low inflation credibility that existed before inflation turned up during the 1960s. The conclusion is that the risks of starting another costly inflation/disinflation cycle could be avoided by monitoring monetary growth and maintaining a sufficiently tight policy to keep inflation low. An environment of credible price stability would allow the economy to function unfettered by inflationary distortions, which is all that can reasonably be expected of monetary policy, and is precisely what should be expected. (1) The file submitted is the data file 9811WD.DAT. (2) These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.

  20. Producer price inflation time series (MM22)

    • ons.gov.uk
    • cy.ons.gov.uk
    csdb, csv, xlsx
    Updated Feb 19, 2025
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    Office for National Statistics (2025). Producer price inflation time series (MM22) [Dataset]. https://www.ons.gov.uk/economy/inflationandpriceindices/datasets/producerpriceindex
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    csv, xlsx, csdbAvailable download formats
    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Producer Price Indices (PPIs) are a series of economic indicators that measure the price movement of goods bought and sold by UK manufacturers.

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ckan.africadatahub.org (2021). kenya inflation - Dataset - ADH Data Portal [Dataset]. https://ckan.africadatahub.org/dataset/kenya-inflation
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kenya inflation - Dataset - ADH Data Portal

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Dataset updated
Sep 10, 2021
Dataset provided by
CKANhttps://ckan.org/
License

U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically

Area covered
Kenya
Description

(i) 12-month inflation: normally considered as inflation rate, is defined as the percentage change in the monthly consumer price index (CPI). For example, the 12-month inflation rate for November 2017 is the percentage change in the CPI of November 2017 and November 2016. (ii) Annual average inflation: is the percentage change in the annual average consumer price index (CPI) of the corresponding months e.g. November 2017 and November 2016. (iii) Source: Kenya National Bureau of Statistics. The latest Kenyan Inflation rates can be downloaded here

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