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

Seung Chan Ahn () and Hyungsik Moon ()
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Seung Chan Ahn: Department of Economics, Arizona State University, Postal: Department of Economics, Arizona State University, Tempe, AZ 85287, USA

No A6-2, 10th International Conference on Panel Data, Berlin, July 5-6, 2002 from International Conferences on Panel Data

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.

Date: 2001-08
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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