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Learning and the Great Moderation

James Bullard and Aarti Singh

No 2009-01, Working Papers from University of Sydney, School of Economics

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: business cycles; regime-switching; Bayesian learning; information (search for similar items in EconPapers)
Date: 2009-02
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http://hdl.handle.net/2123/7092

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