Forecasting substantial data revisions in the presence of model uncertainty

Koop, G.M. and Garratt, Anthony and Vahey, Shaun (2008) Forecasting substantial data revisions in the presence of model uncertainty. Economic Journal, 118 (530). pp. 1128-1144. ISSN 0013-0133 (https://doi.org/10.1111/j.1468-0297.2008.02163.x)

[thumbnail of strathprints007748]
Preview
 Text. Filename: strathprints007748.pdf
Accepted Author Manuscript
Download (356kB)| Preview

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.

ORCID iDs

Koop, G.M. ORCID logoORCID: https://orcid.org/0000-0002-6091-378X, Garratt, Anthony and Vahey, Shaun;
CORE (COnnecting REpositories)