Forecasting German GDP Using Alternative Factor Models Based on Large Datasets

Journal of Forecasting, Vol. 26, pp. 271-302, 2007

Posted: 6 Sep 2007 Last revised: 14 Jun 2008

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Abstract

This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the German economy. One model extracts factors by static principal components analysis; the second model is based on dynamic principal components obtained using frequency domain methods; the third model is based on subspace algorithms for state-space models. Out-of-sample forecasts show that the forecast errors of the factor models are on average smaller than the errors of a simple autoregressive benchmark model. Among the factor models, the dynamic principal component model and the subspace factor model outperform the static factor model in most cases in terms of mean-squared forecast error. However, the forecast performance depends crucially on the choice of appropriate information criteria for the auxiliary parameters of the models. In the case of misspecification, rankings of forecast performance can change severely.

Keywords: factor models, static and dynamic factors, principal components, forecasting accuracy

JEL Classification: E32, C51, C43

Suggested Citation

Schumacher, Christian, Forecasting German GDP Using Alternative Factor Models Based on Large Datasets. Journal of Forecasting, Vol. 26, pp. 271-302, 2007, Available at SSRN: https://ssrn.com/abstract=1011587

Christian Schumacher (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

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