Forecasting substantial data revisions in the presence of model uncertainty

• 成果类型: Article
• 署名作者: Garratt, Anthony; Koop, Gary; Vahey, Shaun P.
• 署名单位: University of London; Birkbeck University London; University of Strathclyde
• 刊物名称: ECONOMIC JOURNAL
• ISSN/ISSBN: 0013-0133
• DOI: 10.1111/j.1468-0297.2008.02163.x
• 发表日期: 2008
• 页码: 1128-1144
• 关键词: real; output

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 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.