Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors: The Case of Stationary and Non-Stationary Regressors and Residuals

Author(s)/Creator(s)

Badi H. Baltagi, Syracuse UniversityFollow
Chihwa Kao, Syracuse UniversityFollow
Long Liu, Syracuse University

Description/Abstract

This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares, fixed effects, first-difference, and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.

Document Type

Working Paper

Date

2007

Keywords

Panel data, OLS, Fixed-effects, First-difference, GLS.

Language

English

Series

Working Papers Series

Disciplines

Mathematics

Additional Information

Harvest from RePEc at http://repec.org

Recommended Citation

Baltagi, Badi H.; Kao, Chihwa; and Liu, Long, "Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors: The Case of Stationary and Non-Stationary Regressors and Residuals" (2007). Center for Policy Research. 72.
https://surface.syr.edu/cpr/72

Source

Metadata from RePEc

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.