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US inflation dynamics on long-range data

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  • Vasilios Plakandaras
  • Periklis Gogas
  • Rangan Gupta
  • Theophilos Papadimitriou

Abstract

In this article, we evaluate inflation persistence in the United States using long-range monthly and annual data. The importance of inflation persistence is crucial to policy authorities and market participants, since the level of inflation persistence provides an indication on the susceptibility of the economy to exogenous shocks. Departing from classic econometric approaches found in the relevant literature, we evaluate inflation persistence through the nonparametric Hurst exponent within both a global and a rolling window framework. Moreover, we expand our analysis to detect the potential existence of chaos in the data generating process, in order to enhance the robustness of our conclusions. Overall, we find that inflation persistence is high from 1775 to 2013 for the annual data-set and from February 1876 to May 2014 in monthly frequency, respectively. Especially from the monthly data-set, the rolling window approach allows us to derive that inflation persistence has reached to historically high levels in the post-Bretton Woods period and remained there ever since.

Suggested Citation

  • Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2015. "US inflation dynamics on long-range data," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3874-3890, August.
  • Handle: RePEc:taf:applec:v:47:y:2015:i:36:p:3874-3890
    DOI: 10.1080/00036846.2015.1019039
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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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