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Learning and heterogeneity in GDP and inflation forecasts

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  • Lahiri, Kajal
  • Sheng, Xuguang

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21448.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:21448

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Keywords: Bayesian learning; Public information; Panel data; Forecast disagreement; Forecast horizon; Content function; Forecast efficiency; GDP; Inflation targeting;

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References

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Cited by:
  1. Clements, Michael P., 2012. "Do professional forecasters pay attention to data releases?," International Journal of Forecasting, Elsevier, vol. 28(2), pages 297-308.
  2. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do Forecasters Inform or Reassure?: Evaluation of the German Real-Time Data," Discussion Papers of DIW Berlin 858, DIW Berlin, German Institute for Economic Research.
  3. 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.
  4. 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.
  5. 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.
  6. 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.

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