Working Paper

Learning about Regime Change

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;

JEL Classification: E13; E32; C63;

https://doi.org/10.24148/wp2020-15

Bibliographic Information

Provider: Federal Reserve Bank of San Francisco

Part of Series: Working Paper Series

Publication Date: 2020-04-15

Number: 2020-15