Journal article
Efficient estimation of approximate factor models via penalized maximum likelihood
Journal of econometrics, Vol.191(1), pp.1-18
03/01/2016
DOI: 10.1016/j.jeconom.2015.10.003
Abstract
We study an approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. For efficient estimations it is essential to estimate a large error covariance matrix. We estimate the common factors and factor loadings based on maximizing a Gaussian quasi-likelihood, through penalizing a large covariance sparse matrix. The weighted ℓ1 penalization is employed. While the principal components (PC) based methods estimate the covariance matrices and individual factors and loadings separately, they require consistent estimation of residual terms. In contrast, the penalized maximum likelihood method (PML) estimates the factor loading parameters and the error covariance matrix jointly. In the numerical studies, we compare PML with the regular PC method, the generalized PC method (Choi 2012) combined with the thresholded covariance matrix estimator (Fan et al. 2013), as well as several related methods, on their estimation and forecast performances. Our numerical studies show that the proposed method performs well in the presence of cross-sectional dependence and heteroskedasticity.
Details
- Title: Subtitle
- Efficient estimation of approximate factor models via penalized maximum likelihood
- Creators
- Jushan Bai - Columbia UniversityYuan Liao - University of Maryland, College Park
- Resource Type
- Journal article
- Publication Details
- Journal of econometrics, Vol.191(1), pp.1-18
- DOI
- 10.1016/j.jeconom.2015.10.003
- ISSN
- 0304-4076
- eISSN
- 1872-6895
- Publisher
- Elsevier B.V
- Number of pages
- 18
- Language
- English
- Date published
- 03/01/2016
- Academic Unit
- Economics
- Record Identifier
- 9984936837602771
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