We thank Frank Schorfheide (the editor) and three anonymous referees for useful suggestions. We also thank Jean Boivin, Yunjong Eo, Kyu Ho Kang, James Morley, and participants at numerous conferences, seminars, and workshops for their comments. In addition, thanks also to S. Boragan Aruoba, Jesus Fernandez-Villaverde, and Juan Rubio-Ramirez for providing their Mathematica code concerning perturbation methods. Singh thanks the Center for Research in Economics and Strategy at the Olin School of Business for financial support. Any views expressed in this article are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis or the Federal Reserve System. Please address correspondence to: Aarti Singh, School of Economics, Faculty of Arts and Social Sciences, H04 - Merewether Building, University of Sydney, NSW 2006, Australia. Phone: +61-2-9351-5250. E-mail: email@example.com.
LEARNING AND THE GREAT MODERATION*
Article first published online: 21 MAY 2012
© (2012) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association
International Economic Review
Volume 53, Issue 2, pages 375–397, May 2012
How to Cite
Bullard, J. and Singh, A. (2012), LEARNING AND THE GREAT MODERATION. International Economic Review, 53: 375–397. doi: 10.1111/j.1468-2354.2012.00685.x
Manuscript received March 2008; revised March 2009.
- Issue published online: 21 MAY 2012
- Article first published online: 21 MAY 2012
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% of a volatility reduction of the magnitude observed in the postwar U.S. data.