An Estimable Dynamic Model of Recreation Behavior with an Application to Great Lakes Angling

This paper develops an estimable model of recreation behavior in which the recreation decision is cast as a dynamic programming problem. The model is illustrated with an application to salmon and trout fishing on Lake Michigan. The application considers various factors affecting the trip decision, including the expected catch, the weather, the opportunity to participate in a fishing derby, and the time elapsed since the last fishing trip. Catch is modeled as a Poisson-distributed random variable. Estimation results are promising, but a number of practical obstacles must be surmounted for the model to be regularly applied in recreation demand analysis.