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Learning about Regime Change

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Abstract

Total factor productivity (TFP) and investment specific technology (IST) growth both exhibit regime-switching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We then develop a general perturbation solution algorithm for a wide class of models with unobserved regime-switching. Using our method, we show that learning about regime-switching alters the responses to regime shifts and intra-regime shocks, increases asymmetries in the responses, generates forecast error bias even with rational agents, and raises the welfare cost of fluctuations.

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  • Andrew Foerster & Christian Matthes, 2020. "Learning about Regime Change," Working Paper Series 2020-15, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:87843
    DOI: 10.24148/wp2020-15
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    Cited by:

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    2. John G. Fernald & Huiyu Li, 2021. "The Impact of COVID on Potential Output," Working Paper Series 2021-09, Federal Reserve Bank of San Francisco.
    3. Janice C. dup Eberly & John dup Fernald, 2022. "Jackson Hole 2022 - Reassessing Economic Constraints: Potential Output (The Impact of COVID on Productivity and Potential Output)," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, August.
    4. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.

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    More about this item

    Keywords

    Bayesian learning; regime switching; technology growth;
    All these keywords.

    JEL classification:

    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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