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

Clements, M. P. (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

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To link to this item DOI: 10.1080/07350015.2015.1081596


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
Divisions:Henley Business School > ICMA Centre
ID Code:41466
Publisher:Taylor & Francis


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