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(2005), pp. 830-840.

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.
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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(2005), pp. 830-840.
Forni, Mario; M., Hallin; M., Lippi; L., Reichlin
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/303531
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