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Regime-Switching Models for Estimating Inflation Uncertainty

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Abstract

This paper constructs regime-switching models for estimating the probability of inflation returning to its relatively high levels of variability and persistence in the 1970s and 1980s. Forecasts and probabilities of extreme events from the models are evaluated against comparable estimates from other statistical models, from surveys, and from financial markets. The paper then uses the models to construct prediction intervals around Federal Reserve Board staff forecasts of PCE price inflation, combining the recent non-parametric forecast error distribution with parametric information from the model. The outer tails of the prediction intervals depend importantly on the probability inflation is in its high-variance, high-persistence regime.

Suggested Citation

  • Nalewaik, Jeremy J., 2015. "Regime-Switching Models for Estimating Inflation Uncertainty," Finance and Economics Discussion Series 2015-93, Board of Governors of the Federal Reserve System (US).
  • Handle: RePEc:fip:fedgfe:2015-93
    DOI: 10.17016/FEDS.2015.093
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    File URL: http://dx.doi.org/10.17016/FEDS.2015.093
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    References listed on IDEAS

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    1. Troy Davig & Taeyoung Doh, 2014. "Monetary Policy Regime Shifts and Inflation Persistence," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 862-875, December.
    2. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    3. Lutz Kilian & Robert J. Vigfusson, 2013. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 78-93, January.
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    More about this item

    Keywords

    Inflation; Markov-Switching; Uncertainty;

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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