Journal article
Feasible generalized least squares for panel data with cross-sectional and serial correlations
Empirical economics, Vol.60(1), pp.309-326
01/2021
DOI: 10.1007/s00181-020-01977-2
Abstract
This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS estimator is more efficient than the ordinary least squares in the presence of heteroskedasticity, serial and cross-sectional correlations. The covariance matrix used for the feasible GLS is estimated via the banding and thresholding method. We establish the limiting distribution of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.
Details
- Title: Subtitle
- Feasible generalized least squares for panel data with cross-sectional and serial correlations
- Creators
- Jushan Bai - Columbia UniversitySung Hoon Choi - Rutgers, The State University of New JerseyYuan Liao - Rutgers, The State University of New Jersey
- Resource Type
- Journal article
- Publication Details
- Empirical economics, Vol.60(1), pp.309-326
- DOI
- 10.1007/s00181-020-01977-2
- ISSN
- 0377-7332
- eISSN
- 1435-8921
- Publisher
- Springer Berlin Heidelberg
- Number of pages
- 18
- Language
- English
- Date published
- 01/2021
- Academic Unit
- Economics
- Record Identifier
- 9984936814602771
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