On the Design of Data Sets for Forecasting with Dynamic Factor Models
ISBN: 978-1-78560-353-2, eISBN: 978-1-78560-352-5
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.
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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, Leeds, pp. 629-662. https://doi.org/10.1108/S0731-905320150000035016
Publisher
:Emerald Group Publishing Limited
Copyright © 2016 Emerald Group Publishing Limited