Learning about Regime Change

Andrew Foerster and Christian Matthes

No 2020-15, Working Paper Series from Federal Reserve Bank of San Francisco

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.

Keywords: Bayesian learning; regime switching; technology growth (search for similar items in EconPapers)
JEL-codes: C63 E13 E32 (search for similar items in EconPapers)
Pages: 47
Date: 2020-04-15
New Economics Papers: this item is included in nep-mac and nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: LEARNING ABOUT REGIME CHANGE (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedfwp:87843

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DOI: 10.24148/wp2020-15

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