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The generalized dynamic factor model: One-sided estimation and forecasting
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DOI：10.1198/016214504000002050.png)
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Abstract
En 中文
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.
Keywords:
dynamic factor model
forecasting
large cross-section
panel data
principal components
time series
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