Bayesian Model Averaging and Jointness Measures for gretl

This paper presents a software package that implements Bayesian model averaging for gretl, the GNU regression, econometrics and time-series library. Bayesian model averaging is a model-building strategy that takes account of model uncertainty in conclusions about estimated parameters. It is an efficient tool for discovering the most probable models and obtaining estimates of their posterior characteristics. In recent years we have observed an increasing number of software packages devoted to Bayesian model averaging for different statistical and econometric software. In this paper, we propose the BMA package for gretl, which is an increasingly popular free, open-source software for econometric analysis with an easy-to-use graphical user interface. We introduce the BMA package for linear regression models with jointness measures proposed by Ley and Steel (2007) and Doppelhofer and Weeks (2009).

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.