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Large-N and Large-T Properties of Panel Data Estimators and the Hausman Test

  • Seung Chan Ahn

    ()

    (Department of Economics, Arizona State University)

  • Hyungsik Roger Moon

    (Department of Economics, University of Southern California)

This paper examines the asymptotic properties of the popular within, GLS estimators and the Hausman test for panel data models with both large numbers of cross-section (N) and time-series (T) observations. The model we consider includes the regressors with deterministic trends in mean as well as time invariant regressors. If a time-varying regressor is correlated with time invariant regressors, the time series of the time varying regressor is not ergodic. Our asymptotic results are obtained considering the dependence of such non-ergodic time-varying regressors. We find that the within estimator is as efficient as the GLS estimator. Despite this asymptotic equivalence, however, the Hausman statistic, which is essentially a distance measure between the two estimators, is well defined and asymptotically \chi^2-distributed under the random effects assumption.

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Paper provided by International Conferences on Panel Data in its series 10th International Conference on Panel Data, Berlin, July 5-6, 2002 with number A6-2.

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Date of creation: Aug 2001
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Handle: RePEc:cpd:pd2002:a6-2
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  1. 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.
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  8. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
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  11. Matthew Higgins & Egon Zakrajsek, 1999. "Purchasing power parity: three stakes through the heart of the unit root null," Staff Reports 80, Federal Reserve Bank of New York.
  12. Cornwell, Christopher & Rupert, Peter, 1988. "Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(2), pages 149-55, April.
  13. 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.
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  15. Breusch, Trevor S & Mizon, Grayham E & Schmidt, Peter, 1989. "Efficient Estimation Using Panel Data," Econometrica, Econometric Society, vol. 57(3), pages 695-700, May.
  16. Amemiya, Takeshi & MaCurdy, Thomas E, 1986. "Instrumental-Variable Estimation of an Error-Components Model," Econometrica, Econometric Society, vol. 54(4), pages 869-80, July.
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