Testing the exogeneity assumption in panel data models with "non classical" disturbances

This paper is concerned with the use of the Durbin-Wu-Hausman test for correlated effects with panel data. The assumptions underlying the construction of the statistic are too strong in many empirical cases. The consequences of deviations from the basic assumptions are investigated. The size distortion is assessed. In the case of measurement error, the Hausman test is found to be a test of the difference in asymptotic biases of between and within group estimators. However, its `size' is sensitive to the relative magnitude of the intra-group and inter-group variations of the covariates, and can be so large as to preclude the use of the statistic in this case. We show to what extent some assumptions can be relaxed in a panel data context and we discuss an alternative robust formulation of the test. Power considerations are presented. <br><br> Keywords; models with panel data, hausman test, minimum variance estimators, quadratic forms in normal variables, monte carlo simulations