文章基本信息

标题：Forecasting Substantial Data Revisions in the Presence of Model Uncertainty
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作者：Anthony Garratt ; Gary Koop ; Shaun P Vahey 等
期刊名称：Birkbeck Working Papers in Economics and Finance / School of Economics, Mathematics and Statistics, Birkbeck College
印刷版ISSN：1745-8587
出版年度：2006
卷号：2006
出版社：London University
摘要：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 paper, 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 heteroskedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements.