On probit versus logit dynamic mixed models for binary panel data

Abstract

In this paper, we consider inferences in a binary dynamic mixed model. The existing estimation approaches mainly estimate the regression effects and the dynamic dependence parameters either through the estimation of the random effects or by avoiding the random effects technically. Under the assumption that the random effects follow a Gaussian distribution, we propose a generalized quasilikelihood (GQL) approach for the estimation of the parameters of the dynamic mixed models. The proposed approach is computationally less cumbersome than the exact maximum likelihood (ML) approach. We also carry out the GQL estimation under two competitive, namely, probit and logit mixed models, and discuss both the asymptotic and small-sample behaviour of their estimators.

Keywords:

• consistent estimation
• correlation parameters
• efficient estimation
• fixed models
• model robustness
• variance of the random effects

Acknowledgements

This research was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada. The authors thank the referee for valuable comments.