Bai, Jushan and Li, Kunpeng (2012): Maximum likelihood estimation and inference for approximate factor models of high dimension.
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Abstract
An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the crosssection 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 likelihoodbased estimation of the model. Consistency, rate of convergence, and limiting distributions are obtained under various identification restrictions. Comparison with the principal component method is made. The likelihoodbased estimators are more efficient than those of principal component based. Monte Carlo simulations show the method is easy to implement and an application to the U.S. yield curves is considered
Item Type:  MPRA Paper 

Original Title:  Maximum likelihood estimation and inference for approximate factor models of high dimension 
Language:  English 
Keywords:  Factor analysis; Approximate factor models; Maximum likelihood; Kalman smoother, Principal components; Inferential theory 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C33  Panel Data Models ; Spatiotemporal Models 
Item ID:  42099 
Depositing User:  Jushan Bai 
Date Deposited:  21 Oct 2012 17:58 
Last Modified:  14 Jan 2017 12:51 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/42099 
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