Learning and heterogeneity in GDP and inflation forecasts

Kajal Lahiri and Xuguang Simon Sheng

MPRA Paper from University Library of Munich, Germany

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, Public information, Panel data, Forecast disagreement, Forecast horizon; Content function; Forecast efficiency; GDP; Inflation targeting (search for similar items in EconPapers)
JEL-codes: C11 E17 (search for similar items in EconPapers)
Date: 2009
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Published in International Journal of Forecasting 26 (2010): pp. 265-292

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Related works:
Journal Article: Learning and heterogeneity in GDP and inflation forecasts (2010)
Working Paper: Learning and Heterogeneity in GDP and Inflation Forecasts (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:21448

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