A QUASI-MAXIMUM LIKELIHOOD APPROACH FOR LARGE, APPROXIMATE DYNAMIC FACTOR MODELS

• 成果类型: Article
• 署名作者: Doz, Catherine; Giannone, Domenico; Reichlin, Lucrezia
• 署名单位: Paris School of Economics; heSam Universite; Universite Pantheon-Sorbonne; Universite Libre de Bruxelles; Center for Economic & Policy Research (CEPR); University of London; London Business School
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00225
• 发表日期: 2012-11
• 页码: 1014-1024
• 关键词: indexes

摘要

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T), going to infinity along any path, and that maximum likelihood is viable for n large. The estimator is robust to misspecification of cross-sectional and time series correlation of the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.