A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Auto correlated Disturbances
Panel data, Spatially auto correlated residuals, Pretest estimator, Maximum-likelihood estimation
Working Papers Series
This paper examines the consequences of model misspecification using a panel data model with spatially auto correlated disturbances. The performance of several maximum likelihood estimators assuming different specifications for this model are compared using Monte Carlo experiments. These include (i) MLE of a random effects model that ignore the spatial correlation; (ii) MLE described in Anselin (1988) which assumes that the individual effects are not spatially auto correlated; (iii) MLE described in Kapoor et al. (2006) which assumes that both the individual effects and the remainder error are governed by the same spatial autocorrelation; (iv) MLE described in Baltagi et al. (2006) which allows the spatial correlation parameter for the individual effects to be different from that of the remainder error term. The latter model encompasses the other models and allows the researcher to test these specifications as restrictions on the general model using LM and LR tests. In fact, based on these tests, we suggest a pretest estimator which is shown to perform well in Monte Carlo experiments, ranking a close second to the true MLE in mean squared error performance.
Baltagi, Badi H.; Egger, Peter; and Pfaffermayr, Michael, "A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Auto correlated Disturbances" (2007). Center for Policy Research. 67.
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