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

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  • Seung Chan Ahn

    ()
    (Department of Economics, Arizona State University)

  • Hyungsik Roger Moon

    (Department of Economics, University of Southern California)

Abstract

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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Bibliographic Info

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. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-58, March.
  2. J. A. Hausman, 1976. "Specification Tests in Econometrics," Working papers 185, Massachusetts Institute of Technology (MIT), Department of Economics.
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  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. 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.
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  8. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
  9. Breusch, Trevor S & Mizon, Grayham E & Schmidt, Peter, 1989. "Efficient Estimation Using Panel Data," Econometrica, Econometric Society, vol. 57(3), pages 695-700, May.
  10. 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.
  11. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, 07.
  12. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
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  14. Baltagi, Badi H & Khanti-Akom, Sophon, 1990. "On Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(4), pages 401-06, Oct.-Dec..
  15. 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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Cited by:
  1. Pablo, Agnese, 2009. "Japan and her dealings with offshoring: An empirical analysis with aggregate data," MPRA Paper 16505, University Library of Munich, Germany.
  2. Herwartz, Helmut & Neumann, Michael H., 2007. "A robust bootstrap approach to the Hausman test in stationary panel data models," Economics Working Papers 2007,29, Christian-Albrechts-University of Kiel, Department of Economics.

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