Learning and Heterogeneity in GDP and Inflation Forecasts

62 Pages Posted: 12 Sep 2008

See all articles by Kajal Lahiri

Kajal Lahiri

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics

Xuguang Simon Sheng

American University

Date Written: September 10, 2008

Abstract

We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.

Keywords: Bayesian learning, GDP, Inflation targeting, Heterogeneity, Forecast Efficiency, Forecast disagreement, Forecast horizon

JEL Classification: C11, E17

Suggested Citation

Lahiri, Kajal and Sheng, Xuguang Simon, Learning and Heterogeneity in GDP and Inflation Forecasts (September 10, 2008). Available at SSRN: https://ssrn.com/abstract=1266219 or http://dx.doi.org/10.2139/ssrn.1266219

Kajal Lahiri (Contact Author)

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics ( email )

Department of Economics
1400 Washington Avenue
Albany, NY 12222
United States
518-442 4758 (Phone)
518-442 4736 (Fax)

HOME PAGE: http://www.albany.edu/~klahiri

Xuguang Simon Sheng

American University ( email )

4400 Massachusetts Avenue, N.W.
Washington, DC 20016-8029
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
248
Abstract Views
924
rank
128,433
PlumX Metrics