BACE : A gretl Package for Model Averaging in Bounded Dependent Variable Models

Abstract

This study presents a fast and consistent software package called BACE (i.e., Bayesian averaging of classical estimates) that offers a model-building strategy for various bounded dependent variable models, including – logit and probit models, ordered logit and pro-bit models, multinomial logistic regression, Poisson regression, Tobit model, interval regression – as well as linear regression. The BACE approach, originally associated with normal linear regression, is a model selection method that combines classical estimation and Bayesian techniques. It solves the problem of computational speed and model uncertainty that arise when dealing with numerous competing advanced statistical models. Our package also provides an implementation of the well-established Bayesian information criterion variants and the latest measures of jointness. We use gretl , a popular, free, and open-source software for econometric analysis that features an easy-to-use graphical user interface.