Learning about Regime Change


Christian Matthes

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2020-15 | December 1, 2021

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 develop a general perturbation solution algorithm for a wide class of models with unobserved regime-switching. Using our method, we show learning about regime-switching fits the data, affects 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.

About the Author
Andrew Foerster
Andrew Foerster is a senior research advisor in the Economic Research Department at the Federal Reserve Bank of San Francisco. Learn more about Andrew Foerster