VAR Estimation and Forecasting When Data Are Subject to Revision

• Published in: Journal of business & economic statistics Vol. 30; no. 2; pp. 181 - 190
• Main Authors: Kishor, N. Kundan, Koenig, Evan F.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis Group 01.04.2012; American Statistical Association; Taylor & Francis Ltd
• Subjects: Analytical estimating; Analytical forecasting; Asymptotic methods; Consistent estimators; Economic forecasts; Economic models; Economic statistics; Error rates; Estimation methods; Forecasting; Forecasting models; GDP; Gross Domestic Product; Real-time data; Real-time estimation; Risk assessment; Special Section on Measurement Error: Theory and Practice; Statistical forecasts; Studies; Truth; Vector autoregression
• ISSN: 0735-0015; 1537-2707
• DOI: 10.1198/jbes.2010.08169

We show that Howrey's method for producing economic forecasts when data are subject to revision is easily generalized to handle the case where data are produced by a sophisticated statistical agency. The proposed approach assumes that government estimates are efficient with a finite lag. It takes no stand on whether earlier revisions are the result of "news" or of reductions in "noise." We present asymptotic performance results in the scalar case and illustrate the technique using several simple models of economic activity. In each case, it outperforms both conventional VAR analysis and the original Howrey method. It produces GDP forecasts that are competitive with those of professional forecasters. Special cases and extensions of the analysis are discussed in a series of appendices that are available online.