Assessing Macro Uncertainty in Real-Time When Data are Subject to Revision

27 Pages Posted: 11 Jan 2015

See all articles by Michael P. Clements

Michael P. Clements

University of Reading - Henley Business School

Date Written: January 6, 2015

Abstract

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

Keywords: in-sample uncertainty, out-of-sample uncertainty, real-time-vintage estimation

JEL Classification: C53

Suggested Citation

Clements, Michael P., Assessing Macro Uncertainty in Real-Time When Data are Subject to Revision (January 6, 2015). Available at SSRN: https://ssrn.com/abstract=2547589 or http://dx.doi.org/10.2139/ssrn.2547589

Michael P. Clements (Contact Author)

University of Reading - Henley Business School ( email )

Whiteknights
Reading, RG6 6BA
United Kingdom

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