GLS Estimation of Dynamic Factor Models

• 成果类型: Article
• 署名作者: Breitung, Joerg; Tenhofen, Joern
• 署名单位: University of Bonn; Deutsche Bundesbank
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm09693
• 发表日期: 2011
• 页码: 1150-1166
• 关键词: sample properties; number

摘要

In this article a simple two-step estimation procedure of the dynamic factor model is proposed. The estimator allows for heteroscedastic and serially correlated errors. It turns out that the feasible two-step estimator has the same limiting distribution as the generalized least squares (GLS) estimator assuming that the covariance parameters are known. In a Monte Carlo study of the small sample properties, we find that the GLS estimators may be substantially more efficient than the usual estimator based on principal components. Furthermore, it turns out that the iterated version of the estimator may feature considerably improved properties in sample sizes usually encountered in practice.