2020-15 | December 2021
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Learning about Regime Change
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.
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Foerster, Andrew, and Christian Matthes. 2020. "Learning about Regime Change," Federal Reserve Bank of San Francisco Working Paper 2020-15. Available at https://doi.org/10.24148/wp2020-15