Dynamic random utility modeling: A Monte Carlo analysis

Applied studies of commercial fishing have largely ignored the intertemporal aspects of repeated site choices. For many fisheries, fishermen might choose a dynamically optimal cruise trajectory rather than myopic day-to-day strategies and a model that ignores these considerations will likely lead to biased parameter estimates and poor policy guidance. A dynamic random utility model is developed that utilizes the same information as static site-choice models but is entrenched in the principles of dynamic optimization. Using Monte Carlo analysis, we evaluate the performance of this estimator as compared to the static model for a variety of simulated fishery types.