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Learning and Heterogeneity in GDP and Inflation Forecasts

  • Kajal Lahiri
  • Xuguang Sheng

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.

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File URL: http://www.albany.edu/economics/research/workingp/2009/Lahiri_Sheng_IJF.pdf
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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number 09-05.

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Date of creation: 2009
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Handle: RePEc:nya:albaec:09-05
Contact details of provider: Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Fax: (518) 442-4736

Order Information: Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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