This note develops an encompassing model for two well known variants of the negative binomial model (the NB1 and NB2 forms). We conclude with an application of the proposed model using the data employed in a recent health care study.

We examine the interaction effect in nonlinear models discussed by Ai and Norton (2003). Tests about partial effects and interaction terms are not necessarily informative in the context of the model. We suggest ways to examine the effects that do not involve statistical testing.

We extend the discrete data latent class literature by explicitly defining a latent variable for class membership as a function of both observables and unobservables, thereby allowing the equations defining the class membership and observed outcomes to be correlated. The procedure is then...

We obtain an LM test for the random effects probit model. In the natural parameterization of the model the necessary derivatives are identically zero under the null hypothesis. After a reparameterization, the feasible LM test is based on generalized residuals.