This paper examines the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. The paper analyzes the effect of a variety of...

The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets....