Learning and the Great Moderation

James Bullard and Aarti Singh

No 7401, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We study a stylized theory of the volatility reduction in the U.S. after 1984 - the Great Moderation - which attributes part of the stabilization to less volatile shocks and another part to more difficult inference on the part of Bayesian households attempting to learn the latent state of the economy. We use a standard equilibrium business cycle model with technology following an unobserved regime-switching process. After 1984, according to Kim and Nelson (1999a), the variance of U.S. macroeconomic aggregates declined because boom and recession regimes moved closer together, keeping conditional variance unchanged. In our model this makes the signal extraction problem more difficult for Bayesian households, and in response they moderate their behavior, reinforcing the effect of the less volatile stochastic technology and contributing an extra measure of moderation to the economy. We construct example economies in which this learning effect accounts for about 30 percent of a volatility reduction of the magnitude observed in the postwar U.S. data.

Keywords: Bayesian learning; Information; Business cycles; Regime-switching (search for similar items in EconPapers)
JEL-codes: D8 E3 (search for similar items in EconPapers)
Date: 2009-08
New Economics Papers: this item is included in nep-bec, nep-cba and nep-dge
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: LEARNING AND THE GREAT MODERATION (2012)
Working Paper: Learning and the Great Moderation (2009)
Working Paper: Learning and the Great Moderation (2009)
Working Paper: Learning and the Great Moderation (2007)
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