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On the Design of Data Sets for Forecasting with Dynamic Factor Models

Gerhard Rünstler (European Central Bank, Frankfurt, Germany)

Dynamic Factor Models

ISBN: 978-1-78560-353-2, eISBN: 978-1-78560-352-5

ISSN: 0731-9053

Publication date: 6 January 2016

Abstract

Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.

Keywords

Acknowledgements

Acknowledgments

The views expressed in this paper are those of the author and do not necessarily reflect the views of the ECB. The author would like to thank Marta Bańbura, Kirstin Hubrich, Christian Schumacher, Bernd Schwaab, and two anonymous referees for their helpful discussions.

Citation

Rünstler, G. (2016), "On the Design of Data Sets for Forecasting with Dynamic Factor Models", Dynamic Factor Models (Advances in Econometrics, Vol. 35), Emerald Group Publishing Limited, Bingley, pp. 629-662. https://doi.org/10.1108/S0731-905320150000035016

Publisher

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Emerald Group Publishing Limited

Copyright © 2016 Emerald Group Publishing Limited