The Generalized Dynamic Factor Model one-sided estimation and forecasting

Mario Forni, Universit`a di Modena and CEPR
Marc Hallin ISRO, ECARES, and D´epartement de Math´ematique Universit´e Libre de Bruxelles
Marco Lippi Universit`a di Roma La Sapienza
and
Lucrezia Reichlin ECARES, Universit´e Libre de Bruxelles and CEPR

Abstract

This paper proposes a new forecasting method which makes use of information from a
large panel of time series. As in Forni, Hallin, Lippi and Reichlin (2000), and in Stock
and Watson (2002a,b), the method is based on a dynamic factor model. We argue that
our method improves upon a standard principal component predictor in that, first, it fully
exploits all the dynamic covariance structure of the panel and, second, it weights the variables
according to their estimated signal-to-noise ratio. We provide asymptotic results for our
optimal forecast estimator and show that in finite samples our forecast outperforms the
standard principal components predictor.

http://www.economia.unimore.it/forni_mario/one-sided.pdf