Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/154326
Authors: 
Rünstler, Gerhard
Year of Publication: 
2016
Series/Report no.: 
ECB Working Paper 1893
Abstract: 
Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The 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.
Subjects: 
dynamic factor models
forecasting
LARS
variable selection
JEL: 
E37
C53
C51
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-2022-3
Document Type: 
Working Paper

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