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

• 成果类型: Article
• 署名作者: Forni, M; Hallin, M; Lippi, M; Reichlin, L
• 署名单位: Universita di Modena e Reggio Emilia; Universite Libre de Bruxelles; Universite Libre de Bruxelles; Sapienza University Rome
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000002050
• 发表日期: 2005
• 页码: 830-840
• 关键词: multivariate stochastic processes; prediction theory; arbitrage; number

摘要

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.