MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE FOR APPROXIMATE FACTOR MODELS OF HIGH DIMENSION

• 成果类型: Article
• 署名作者: Bai, Jushan; Li, Kunpeng
• 署名单位: Columbia University; Nankai University; Capital University of Economics & Business
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00519
• 发表日期: 2016-05
• 页码: 298-309
• 关键词: dynamic factor models; monetary-policy; em algorithm; arbitrage; number; return

摘要

An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or even greater than the sample size. Thus, a large number of parameters exist under a high-dimensional approximate factor model. Most widely used approaches to estimation are principal component based. This paper considers the maximum likelihood-based estimation of the model. Consistency, rate of convergence, and limiting distributions are obtained under various identification restrictions. Monte Carlo simulations show that the likelihood method is easy to implement and has good finite sample properties.