Learning and Heterogeneity in GDP and Inflation Forecasts

Kajal Lahiri and Xuguang Simon Sheng

Discussion Papers from University at Albany, SUNY, Department of Economics

Abstract: Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different patterns of forecast accuracy. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs, 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, and help to explain the relative forecasting accuracy of these two macroeconomic variables.

Date: 2009
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-for and nep-mac
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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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