期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 100, 期 471, 页码 830-840出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/016214504000002050
关键词
dynamic factor model; forecasting; large cross-section; panel data; principal components; time series
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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