Gaussian Quadrature Versus Simulation for the Estimation of Random Parameters

Abstract

In environmental economics, numerical simulation using random draws is the method most commonly used to estimate joint probabilities of individual choices in discrete-choice, random-parameters models. This paper compares simulation to another method of estimation, Gaussian quadrature, on the basis of speed and accuracy. The comparison is done using stated preference data consisting of the answers to choice questions for fishing in Green Bay, a large bay on Lake Michigan. Each sampled individual chose between a pair of Green Bay scenarios with different fishing conditions. Quadrature is found to be as accurate as simulation based on random draws, but Gaussian quadrature attains stability in estimated parameters considerably faster.