Forecasting German GDP using alternative factor models based on large datasets

Forecasting German GDP using alternative factor models based on large datasets

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Article ID: iaor20082577
Country: United Kingdom
Volume: 26
Issue: 4
Start Page Number: 271
End Page Number: 302
Publication Date: Jul 2007
Journal: International Journal of Forecasting
Authors:
Keywords: time series & forecasting methods, forecasting: applications
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.

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