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Forecasting Substantial Data Revisions in the Presence of Model Uncertainty

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  • Anthony Garratt
  • Gary Koop
  • ShaunP. Vahey

Abstract

A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable press attention, provoked a public enquiry and prompted a number of reforms to UK statistical reporting procedures. In this article, we compute the probability of 'substantial revisions' that are greater (in absolute value) than the controversial 2003 revision. The predictive densities are derived from Bayesian model averaging over a wide set of forecasting models including linear, structural break and regime-switching models with and without heteroscedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements. Copyright � The Author(s). Journal compilation � Royal Economic Society 2008.

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 118 (2008)
Issue (Month): 530 (07)
Pages: 1128-1144

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Handle: RePEc:ecj:econjl:v:118:y:2008:i:530:p:1128-1144

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  16. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
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  19. Anthony Garratt & Gary Koop & Shaun P. Vahey, 2006. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Birkbeck Working Papers in Economics and Finance 0617, Birkbeck, Department of Economics, Mathematics & Statistics.
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