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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

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.

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Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 93.

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Length: 76 pages
Date of creation: May 2007
Date of revision:
Handle: RePEc:max:cprwps:93
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  1. Chi-Young Choi; Ling Hu; Masao Ogaki, 2004. "A Spurious Regression Approach to Estimating Structural Parameters," Econometric Society 2004 Far Eastern Meetings 555, Econometric Society.
  2. Sentana, Enrique, 1997. "Estimation of a Triangular, Seemingly Unrelated, Regression System by OLS," Econometric Theory, Cambridge University Press, vol. 13(03), pages 463-463, June.
  3. Badi H.BALTAGI & Qi LI, 1997. "Monte Carlo Results on Pure and Pretest Estimators of an Error Component Model with Autocorrelated Disturbances," Annales d'Economie et de Statistique, ENSAE, issue 48, pages 69-82.
  4. Joerg Breitung & M. Hashem Pesaran, 2005. "Unit Roots and Cointegration in Panels," CESifo Working Paper Series 1565, CESifo Group Munich.
  5. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
  6. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  7. Badi H. Baltagi & Chihwa Kao, 2000. "Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey," Center for Policy Research Working Papers 16, Center for Policy Research, Maxwell School, Syracuse University.
  8. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
  9. Choi, In, 2002. "Instrumental variables estimation of a nearly nonstationary, heterogeneous error component model," Journal of Econometrics, Elsevier, vol. 109(1), pages 1-32, July.
  10. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
  11. Boozer, Michael A., 1997. "Econometric Analysis of Panel Data Badi H. Baltagi Wiley, 1995," Econometric Theory, Cambridge University Press, vol. 13(05), pages 747-754, October.
  12. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
  13. Beach, Charles M & MacKinnon, James G, 1978. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society, vol. 46(1), pages 51-58, January.
  14. Maeshiro, Asatoshi, 1976. "Autoregressive Transformation, Trended Independent Variables and Autocorrelated Disturbance Terms," The Review of Economics and Statistics, MIT Press, vol. 58(4), pages 497-500, November.
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