On the Design of Data Sets for Forecasting with Dynamic Factor Models
AbstractForecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use forecast weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to forecasting euro area, German, and French GDP growth from unbalanced monthly data suggest that both forecast weights and least angle regressions result in improved forecasts. Overall, forecast weights provide yet more robust results.
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Bibliographic InfoPaper provided by WIFO in its series WIFO Working Papers with number 376.
Length: 26 pages
Date of creation: 13 Jul 2010
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-21 (All new papers)
- NEP-CBA-2010-08-21 (Central Banking)
- NEP-ECM-2010-08-21 (Econometrics)
- NEP-ETS-2010-08-21 (Econometric Time Series)
- NEP-FOR-2010-08-21 (Forecasting)
- NEP-ORE-2010-08-21 (Operations Research)
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