On the design of data sets for forecasting with dynamic factor models

Gerhard Rünstler ()

No 1893, Working Paper Series from European Central Bank

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. JEL Classification: E37, C53, C51

Keywords: dynamic factor models; forecasting; LARS; variable selection (search for similar items in EconPapers)
Date: 2016-04
New Economics Papers: this item is included in nep-ets and nep-for
Note: 339116
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Citations: View citations in EconPapers (4)

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Related works:
Chapter: On the Design of Data Sets for Forecasting with Dynamic Factor Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20161893

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