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

Gerhard Rünstler ()

No 376, WIFO Working Papers from WIFO

Abstract: Forecasts 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.

Keywords: KP_Berichte_Analysen (search for similar items in EconPapers)
Pages: 26 pages
Date: 2010-07
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-ore
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https://www.wifo.ac.at/wwa/pubid/40093 abstract (text/html)

Related works:
Chapter: 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 (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:wfo:wpaper:y:2010:i:376

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