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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

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  • Badi H. Baltagi
  • Chihwa Kao
  • Long Liu

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. Copyright The Author(s). Journal compilation Royal Economic Society 2008

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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 11 (2008)
Issue (Month): 3 (November)
Pages: 554-572

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Handle: RePEc:ect:emjrnl:v:11:y:2008:i:3:p:554-572

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Cited by:
  1. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
  2. Badi H. Baltagi & Chihwa Kao & Sanggon Na, 2011. "Test Of Hypotheses In Panel Data Models When The Regressor And Disturbances Are Possibly Nonstationary," Center for Policy Research Working Papers 128, Center for Policy Research, Maxwell School, Syracuse University.

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