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Inflation Dynamics in the US -A Nonlinear Perspective

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  • Bob Nobay

    ()

  • Ivan Paya
  • David A. Peel

Abstract

A stylized fact of US inflation dynamics is one of extreme persistence and possible unit root behavior. If so, the implications for macroeconomics and monetary policy are somewhat unpalatable. Our econometric analysis proposes a parsimonious representation of the inflation process, the nonlinear ESTAR, rather than the IMA process with time-varying parameters as in Stock and Watson (2007). The empirical results confirm a number of the key features such as regime changes and implicit Federal Reserve inflationtargets. We address the issue of whether the source of the Great Moderation can be ascribed to good luck rather than good policy.

Suggested Citation

  • Bob Nobay & Ivan Paya & David A. Peel, 2007. "Inflation Dynamics in the US -A Nonlinear Perspective," FMG Discussion Papers dp601, Financial Markets Group.
  • Handle: RePEc:fmg:fmgdps:dp601
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    References listed on IDEAS

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    1. Harris, David & McCabe, Brendan & Leybourne, Stephen, 2003. "Some Limit Theory For Autocovariances Whose Order Depends On Sample Size," Econometric Theory, Cambridge University Press, vol. 19(05), pages 829-864, October.
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    Cited by:

    1. Tierney, Heather L.R., 2011. "Real-time data revisions and the PCE measure of inflation," Economic Modelling, Elsevier, vol. 28(4), pages 1763-1773, July.
    2. Carlos Usabiaga & Diego Romero-Ávila, 2012. "New Disaggregate Evidence on Spanish Inflation Persistence," EcoMod2012 3800, EcoMod.
    3. Diego Romero-Ávila & Carlos Usabiaga, 2012. "Disaggregate evidence on Spanish inflation persistence," Applied Economics, Taylor & Francis Journals, vol. 44(23), pages 3029-3046, August.

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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