A Bayesian variable selection procedure is used to control for uncertainty in
the specification of a recreational demand model. Specifically, we propose a model that
draws on the Bayesian paradigm to integrate the variable selection process into model estimation
and to reflect the accompanying uncertainty about which is the best specification in
subsequent counterfactual predictions. The advantage of this procedure over previous non-
Bayesian approaches is that it overcomes the problem of pre-testing in specification searches.
In our application, evaluating demand for recreational lake usage in Iowa, we find clear evidence
that site attributes, such as lakes size, handicap facilities and wake restrictions, do
impact lake usage. There is also evidence that water quality matters in household recreation
choices. Indeed, contrary to Abidoye et al. (Am J Agricult Econ, 2012), in which only a single
functional form is considered,we find clear evidence thatwater quality matters, with posterior
probability of less that 10%associated with amodel without anywater quality variables. This
suggests that the flexibility that the Bayesian variable selection model affords in capturing
the linkage between recreation demand and site characteristics can be important.