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Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model


  • Kruiniger, Hugo


This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects. The paper first studies the asymptotic properties of MaCurdy's [MaCurdy, T., 1982. The use of time series processes to model the time structure of earnings in a longitudinal data analysis. Journal of Econometrics 18, 83-114] First Difference Maximum Likelihood (FDML) estimator for the covariance stationary panel AR(1)/unit root model with fixed effects, viz. yi,t=[rho]yi,t-1+(1-[rho])[mu]i+[epsilon]i,t, under a variety of asymptotic plans. Subsequently, the paper shows through Monte Carlo simulations for panels of various dimensions the favourable finite sample properties of the FDMLE for [rho] as compared to those of a number of alternative fixed effects ML estimators for [rho] under covariance stationarity and normality of the data. The paper also discusses panel unit root test procedures that are based on the FDMLE. A Monte Carlo study conducted for one version of these tests reveals that it has very good size and power properties in comparison with alternative panel unit root tests.

Suggested Citation

  • Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
  • Handle: RePEc:eee:econom:v:144:y:2008:i:2:p:447-464

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    References listed on IDEAS

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