View article

Recreation demand models are typically plagued by limited information on site attributes. If these unobserved site attributes are correlated with the observed characteristics and/or the travel cost variable, the resulting parameter estimates are likely to be biased. We develop a Bayesian approach to estimating a model that incorporates a full set of alternative‐specific constants, insulating the key travel cost parameter from the influence of unobservables. The proposed posterior simulator can be used in the mixed logit framework in which some parameters of the conditional utility function are random. We apply the estimation procedures to data from the Iowa Lakes Project.