Assessing macro uncertainty in real-time when data are subject to revision

Clements, M. P. ORCID: https://orcid.org/0000-0001-6329-1341 (2017) Assessing macro uncertainty in real-time when data are subject to revision. Journal of Business & Economic Statistics, 35 (3). pp. 420-433. ISSN 0735-0015 doi: 10.1080/07350015.2015.1081596

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/41466
Identification Number/DOI	10.1080/07350015.2015.1081596
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Publisher	Taylor & Francis
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