The Robustness of Estimators for Dynamic Panel Data Models to Misspecification

Transition from economic theory to a testable form of model invariably involves the use of certain "simplifying assumptions." If, however, these are not valid, misspecified models result. This article considers estimation of the dynamic linear panel data model, which often forms the basis of testable economic hypotheses. The estimators of such a model are frequently similarly based on certain assumptions which appear to be often untenable in practice. Here, the performance of these estimators is analyzed in scenarios where the theoretically required conditions are not met. Specifically, we consider three such instances of serial correlation of the idiosyncratic disturbance terms; correlation of the idiosyncratic disturbance terms and explanatory variables; and, finally, cross-sectional dependence (as a robustness check to these findings, we also consider correlations between observed and unobserved heterogeneity terms). The major findings are that the limited tests readily available tend to have poor power properties and that estimators' performance varies greatly across scenarios. In such a wide array of experiments, it is difficult to pick-out just one "winner." However, a robust estimator across all experiments and parameter settings was a variant of the Wansbeek–Bekker estimator. This is a significant finding, as this estimator is infrequently used in practice. When the experiments are extended to include correlations between observed and unobserved heterogeneity terms, one might also consider, for across-the-board performance, the Blundell and Bond estimator