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

Assessing macro uncertainty in real-time when data are subject to revision
Document type
Discussion paper
Author(s)
Clements, Michael
Publisher
Henley School of Management
Date of publication
1 January 2015
Series
Discussion Paper; Number ICM-2015-02
Subject(s)
Trends: economic, social and technology trends affecting business, Management & leadership: including strategy, public sector management, operations and production
Collection
Business and management
Material type
Reports

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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 then used to investigate small-sample properties.

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