This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic factor model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also 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.

The generalized dynamic factor model: One-sided estimation and forecasting / Forni, Mario; M., Hallin; M., Lippi; L., Reichlin. - In: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. - ISSN 0162-1459. - STAMPA. - 100:471(2005), pp. 830-840. [10.1198/016214504000002050]

The generalized dynamic factor model: One-sided estimation and forecasting

FORNI, Mario;
2005

Abstract

This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic factor model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also 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.
2005
100
471
830
840
The generalized dynamic factor model: One-sided estimation and forecasting / Forni, Mario; M., Hallin; M., Lippi; L., Reichlin. - In: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. - ISSN 0162-1459. - STAMPA. - 100:471(2005), pp. 830-840. [10.1198/016214504000002050]
Forni, Mario; M., Hallin; M., Lippi; L., Reichlin
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/303531
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