Forecasting Substantial Data Revisions in the Presence of Model Uncertainty

17 Pages Posted: 18 Jun 2008

See all articles by Anthony Garratt

Anthony Garratt

University of Warwick

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics

Shaun P. Vahey

Reserve Bank of New Zealand

Date Written: 0000

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.

Suggested Citation

Garratt, Anthony and Koop, Gary and Vahey, Shaun P., Forecasting Substantial Data Revisions in the Presence of Model Uncertainty (0000). The Economic Journal, Vol. 118, Issue 530, pp. 1128-1144, July 2008. Available at SSRN: https://ssrn.com/abstract=1147269 or http://dx.doi.org/10.1111/j.1468-0297.2008.02163.x

Anthony Garratt (Contact Author)

University of Warwick ( email )

West Midlands, CV4 7AL
United Kingdom

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics ( email )

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

Shaun P. Vahey

Reserve Bank of New Zealand ( email )

2 The Terrace
P.O. Box 2498
Wellington, 6011
New Zealand

HOME PAGE: http://www.rbnz.govt.nz/research/profiles/1863220.html

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