We thank Dean Croushore, James Mitchell, Athanasios Orphanides, Adrian Pagan, Simon Potter, Heather Robinson, Andrew Scott, Norman Swanson, Simon van Norden, James Yetman and two referees for helpful comments. We are also grateful to seminar participants at the Society for Computational Economics 2005 meetings, the CIRANO Data Revisions Workshop, University of New South Wales, University of Otago, RBNZ, Norges Bank, FRB San Francisco and the North American Summer Econometric Society Meetings 2006. Financial support from the ESRC (Research Grant No RES-000-22-1342) is acknowledged gratefully. The views in this article do not reflect those of the Reserve Bank of New Zealand or Norges Bank.
Forecasting Substantial Data Revisions in the Presence of Model Uncertainty*
Article first published online: 28 JUN 2008
© The Author(s). Journal compilation © Royal Economic Society 2008
The Economic Journal
Volume 118, Issue 530, pages 1128–1144, July 2008
How to Cite
Garratt, A., Koop, G. and Vahey, S. P. (2008), Forecasting Substantial Data Revisions in the Presence of Model Uncertainty. The Economic Journal, 118: 1128–1144. doi: 10.1111/j.1468-0297.2008.02163.x
- Issue published online: 28 JUN 2008
- Article first published online: 28 JUN 2008
- Submitted: 7 February 2006 Accepted: 8 December 2006
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.