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Learning and Heterogeneity in GDP and Inflation Forecasts

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  • Kajal Lahiri
  • Xuguang Sheng

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.

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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

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
Phone: (518) 442-4735
Fax: (518) 442-4736

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Cited by:
  1. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
  2. Tara M. Sinclair & Edward N. Gamber & H.O. Stekler & Elizabeth Reid, 2008. "Jointly Evaluating the Federal Reserve’s Forecasts of GDP Growth and Inflation," Working Papers 2008-002, The George Washington University, Department of Economics, Research Program on Forecasting, revised Mar 2011.
  3. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
  4. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do forecasters inform or reassure? Evaluation of the German real-time data," KOF Working papers 09-215, KOF Swiss Economic Institute, ETH Zurich.
  5. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
  6. Clements, Michael P, 2011. "Do Professional Forecasters Pay Attention to Data Releases?," The Warwick Economics Research Paper Series (TWERPS) 956, University of Warwick, Department of Economics.

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