Distribution-free estimation of some nonlinear panel data models

This paper studies distribution-free estimation of some multiplicative unob served components panel data models. One class of estimators requires only specification of the conditional mean; in particular, the multinomial quasi -conditional maximum likelihood estimator is shown to be consistent when on ly the conditional mean in the unobserved effects model is correctly specif ied. Additional orthogonality conditions can be used in a method of moments framework. A second class of problems specifies the conditional mean, cond itional variances, and conditional covariances. Using the notion of a condi tional linear predictor, it is shown that specification of conditional seco nd moments implies further orthogonality conditions in the observable data that can be exploited for efficiency gains. This has applications to both c ount and gamma-type panel data regression models. (C) 1999 Elsevier Science S.A. All rights reserved.