Model selection criteria in beta regression with varying dispersion

ABSTRACT

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.

Keywords:

• Beta regression
• Model selection criteria
• Monte Carlo simulation
• Varying dispersion

Mathematics Subject Classification:

• 62J99
• 62F07
• 94A17

Acknowledgments

We gratefully acknowledge partial financial support from CAPES, CNPq, and FAPERGS, Brazil. We also thank two anonymous referees for their comments and suggestions.

Notes

1 Notice that σ is a dispersion, not precision, parameter.