  Regression plays a key role in many research areas and its variable selection
is a classic and major problem. This study emphasizes cost of predictors to be
purchased for future use, when we select a subset of them. Its economic aspect
is naturally formalized by the decision-theoretic approach. In addition, two
Bayesian approaches are proposed to address uncertainty about model parameters
and models: the restricted and extended approaches, which lead us to rethink
about model averaging. From objective, rule-based, or robust Bayes point of
view, the former is preferred. Proposed method is applied to three popular
datasets for illustration.
