Instead of choosing among many specifications, BMA estimates are obtained as a weighted average of the estimates from each of the possible models in the model space with weights proportional to the marginal likelihood of the dependent variable in each model. The resulting average model has better predictive accuracy than single models (Hoeting et al., Once the explanatory variables are identified via BMA, an ordinary least squares estimation7 with robust standard errors is used. The next section presents the data used to implement the estimation strategy.