The Annals of Statistics

Statistical analysis of factor models of high dimension

Jushan Bai and Kunpeng Li

Full-text: Open access

Abstract

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.

Article information

Source
Ann. Statist., Volume 40, Number 1 (2012), 436-465.

Dates
First available in Project Euclid: 16 April 2012

Permanent link to this document
https://projecteuclid.org/euclid.aos/1334581749

Digital Object Identifier
doi:10.1214/11-AOS966

Mathematical Reviews number (MathSciNet)
MR3014313

Zentralblatt MATH identifier
1246.62144

Subjects
Primary: 62H25: Factor analysis and principal components; correspondence analysis
Secondary: 62F12: Asymptotic properties of estimators

Keywords
High-dimensional factor models maximum likelihood estimation factors factor loadings idiosyncratic variances principal components

Citation

Bai, Jushan; Li, Kunpeng. Statistical analysis of factor models of high dimension. Ann. Statist. 40 (2012), no. 1, 436--465. doi:10.1214/11-AOS966. https://projecteuclid.org/euclid.aos/1334581749


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Supplemental materials

  • Supplementary material: Supplement to “Statistical analysis of factor models of high dimension”. In this supplement we provide the detailed proofs for Theorems 5.1–5.4 and 6.1. We also give a simple and direct proof that the EM solutions satisfy the first order conditions. Remarks are given on how to make use of matrix properties to write a faster computer program.