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Regime-Switching Models for Estimating Inflation Uncertainty

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  • Jeremy J. Nalewaik

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

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

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    1. Rüdiger Bachmann & Tim O. Berg & Eric R. Sims, 2015. "Inflation Expectations and Readiness to Spend: Cross-Sectional Evidence," American Economic Journal: Economic Policy, American Economic Association, vol. 7(1), pages 1-35, February.
    2. Kim, Chang-Jin, 1993. "Unobserved-Component Time Series Models with Markov-Switching Heteroscedasticity: Changes in Regime and the Link between Inflation Rates and Inflation Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 341-349, July.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. 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.
    5. 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.
    6. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    7. Markku Lanne, 2006. "Nonlinear dynamics of interest rate and inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1157-1168, December.
    8. 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.
    9. Evans, Martin & Wachtel, Paul, 1993. "Inflation Regimes and the," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 475-511, August.
    10. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
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    Cited by:

    1. Mustafa Akay & Berat Bayram & Abdullah Kazdal & Muhammed Hasan Yilmaz, 2020. "Investigating Regime-Dependent Dynamics in Country Risk Premium: Evidence from Turkey and Emerging Markets," CBT Research Notes in Economics 2008, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    2. Nicolas Pesci & Jean-Philippe Aguilar & Victor James & Fabien Rouillé, 2022. "Inflation Forecasts and European Asset Returns: A Regime-Switching Approach," JRFM, MDPI, vol. 15(10), pages 1-20, October.

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    More about this item

    Keywords

    Inflation; Markov-Switching; Uncertainty;
    All these keywords.

    JEL classification:

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

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