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Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?

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Author Info

  • Christian Schumacher

    ()
    (Deutsche Bundesbank)

  • Christian Dreger

    ()
    (Universität Halle)

Abstract

This paper discusses a large-scale factor model for the German economy. Following the recent literature, a data set of 121 time series is used to determine the factors by principal component analysis. The factors enter a linear dynamic model for German GDP. To evaluate its empirical properties, the model is compared with alternative univariate and multivariate models. These simpler models are based on regression techniques and considerably smaller data sets. Empirical forecast tests show that the large-scale factor model almost always encompasses its rivals. Moreover, out-of-sample forecasts of the large-scale factor model have smaller prediction errors than the forecasts of the alternative models. However, these advantages are not statistically significant, as a test for equal forecast accuracy shows. Therefore, the efficiency gains of using a large data set with this kind of factor models seem to be limited.

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Bibliographic Info

Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 224 (2004)
Issue (Month): 6 (November)
Pages: 731-750

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Handle: RePEc:jns:jbstat:v:224:y:2004:i:6:p:731-750

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Related research

Keywords: Factor models; principal components; forecasting accuracy;

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References

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