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Learning about Regime Change

Author

Listed:
  • Andrew Foerster
  • Christian Matthes

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.

Suggested Citation

  • 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:

    1. Jason Choi & Andrew Foerster, . "Optimal Monetary Policy Regime Switches," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics.
    2. Jason Choi & Andrew Foerster, . "Optimal Monetary Policy Regime Switches," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics.

    More about this item

    Keywords

    Bayesian learning; regime switching; technology growth;

    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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