Forecasting Substantial Data Revisions in the Presence of Model Uncertainty

Anthony Garratt (), Gary Koop and Shaun Vahey

No 617, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

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 paper, 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 heteroskedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements.

Keywords: Revisions; Structural Breaks; Regime Switching; Model Uncertainty; Bayesian Model Averaging; Predictive Densities. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E01 (search for similar items in EconPapers)
Date: 2006-12
New Economics Papers: this item is included in nep-ets, nep-for and nep-mac
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Citations: View citations in EconPapers (11)

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https://eprints.bbk.ac.uk/id/eprint/26926 First version, 2006 (application/pdf)

Related works:
Journal Article: Forecasting Substantial Data Revisions in the Presence of Model Uncertainty (2008)
Journal Article: Forecasting Substantial Data Revisions in the Presence of Model Uncertainty (2008)
Working Paper: Forecasting Substantial Data Revisions in the Presence of Model Uncertainty (2006)
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