Learning and Judgment Shocks in U.S. Business Cycles

36 Pages Posted: 4 Mar 2011

See all articles by James Murray

James Murray

University of Wisconsin - La Crosse – Department of Economics

Date Written: March 2, 2011


This paper examines the role of judgment shocks in combination with other structural shocks in explaining post-war economic volatility within the context of a New Keynesian model. Agents form expectations using constant gain learning then augment these forecasts with judgment. These judgments may be interpreted as a reaction to current news stories or policy announcements that would influence people's expectations. I allow for the possibility that these judgments be informatively based on information about structural shocks, but judgment itself may also be subject to its own stochastic shocks. I estimate a standard New Keynesian model that includes these shocks using Bayesian simulation methods. To aid in identifying expectational shocks from other structural shocks I include data on professional forecasts along with data on output gap, inflation, and interest rates. I find judgment is largely not informed by macroeconomic fundamentals; most of the variability in judgment is explained by its own stochastic shocks. Impulse response functions from the estimated model illustrate how shocks to judgment destabilize the economy and explain business cycle fluctuations.

Keywords: Learning, Judgment, Add-Factors, New Keynesian Model, Metropolis-Hastings

JEL Classification: C13, E31, E50

Suggested Citation

Murray, James, Learning and Judgment Shocks in U.S. Business Cycles (March 2, 2011). Available at SSRN: https://ssrn.com/abstract=1774962 or http://dx.doi.org/10.2139/ssrn.1774962

James Murray (Contact Author)

University of Wisconsin - La Crosse – Department of Economics ( email )

1725 State Street
La Crosse, WI 54601
United States
608-785-5140 (Phone)

HOME PAGE: http://www.murraylax.org

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics