Forecasting Substantial Data Revisions in the Presence of Model Uncertainty
利用贝叶斯模型平均方法，结合多种预测模型，计算英国GDP初步数据出现重大修正的概率，发现忽略非线性和模型不确定性会导致预测失真。
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.