Asymptotic properties of estimators for the linear panel regression model with random individual effects and serially correlated errors: The case of stationary and non-stationary regressors and residuals

Badi H. Baltagi, Chihwa Kao, Long Liu

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with random 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 (OLS), fixed effects (FE), first-difference (FD) 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.

Original languageEnglish (US)
Pages (from-to)554-572
Number of pages19
JournalEconometrics Journal
Volume11
Issue number3
DOIs
StatePublished - 2008

Keywords

  • First-difference
  • Fixed-effects
  • GLS
  • OLS
  • Panel data

ASJC Scopus subject areas

  • Economics and Econometrics

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