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

Gerhard Rünstler ()

A chapter in Dynamic Factor Models, 2016, vol. 35, pp 629-662 from Emerald Group Publishing Limited

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: Dynamic factor models; forecasting; variable selection; E37; C53; C51 (search for similar items in EconPapers)
Date: 2016
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Related works:
Working Paper: On the design of data sets for forecasting with dynamic factor models (2016)
Working Paper: On the Design of Data Sets for Forecasting with Dynamic Factor Models (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320150000035016

DOI: 10.1108/S0731-905320150000035016

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