Working Papers

2020-15 | April 2020

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Learning about Regime Change

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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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Article Citation

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