Data Revisions and Real-Time Probabilistic Forecasting of Macroeconomic Variables.

37 Pages Posted: 24 Jul 2017

See all articles by Michael P. Clements

Michael P. Clements

University of Reading - Henley Business School

Ana Beatriz Galvão

Bloomberg Economics; University of Warwick - Warwick Business School

Date Written: April 5, 2017

Abstract

Macroeconomic data are subject to revision over time as later vintages are released, yet the usual way of generating real-time out-of-sample forecasts from models effectively makes no allowance for this form of data uncertainty. We analyse a simple method which has been used in the context of point forecasting, and does make an allowance for data uncertainty. This method is applied to density forecasting in the presence of time-varying heteroscedasticity, and is shown in principle to improve real-time density forecasts. We show that the magnitude of the improvements that might be achieved from this method depend on the nature of the data revisions.

Keywords: real-time forecasting, inflation and output growth predictive densities, real- time-vintages, time-varying heteroscedasticity.

JEL Classification: C53

Suggested Citation

Clements, Michael P. and Galvão, Ana Beatriz, Data Revisions and Real-Time Probabilistic Forecasting of Macroeconomic Variables. (April 5, 2017). Available at SSRN: https://ssrn.com/abstract=3005946 or http://dx.doi.org/10.2139/ssrn.3005946

Michael P. Clements (Contact Author)

University of Reading - Henley Business School ( email )

Whiteknights
Reading, RG6 6BA
United Kingdom

Ana Beatriz Galvão

Bloomberg Economics ( email )

3 Queen Victoria Street
London, EC4N 4TQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/anabgalvao/

University of Warwick - Warwick Business School ( email )

Coventry CV4 7AL
United Kingdom

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