Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors

Citation:
Abonazel, M. R., "Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors", Journal of Statistics Applications and Probability, vol. 7, issue 3, pp. 423-434, 2018.

Abstract:

This paper considers stochastic parameter panel data models when the errors are first-order serially correlated. The feasible generalized least squares (FGLS) and simple mean group (SMG) estimators for these models have been reviewed and examined. The efficiency comparisons for these estimators have been carried when the regression parameters are stochastic, non-stochastic, and mixed stochastic. Monte Carlo simulation study and a real data application are given to evaluate the performance of FGLS and SMG estimators. The results indicate that, in small samples, SMG estimator is more reliable in most situations than FGLS estimators, especially when the model includes one or more non-stochastic parameter.

Notes:

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