The generalized dynamic factor model: One-sided estimation and forecasting
The generalized dynamic factor model: One-sided estimation and forecasting
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DOI：
10.1198/016214504000002050
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发表时间：
2005-09-01
影响因子：
3.7
通讯作者：
Reichlin, L
中科院分区：
文献类型：
--
作者：
Forni, M;Hallin, M;Reichlin, L
本文提出了一种新的预测方法，该方法利用大量时间序列中的信息。与早期的方法一样，我们的方法基于动态因子模型。我们认为，我们的方法改进了标准主成分预测器，因为它充分利用了面板的所有动态协方差结构，并且还根据变量的估计信噪比对变量进行加权。我们为最佳预测估计器提供渐近结果，并表明在有限样本中，我们的预测优于标准主成分预测器。
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.