ALTERNATIVE COMPUTATIONAL APPROACHES TO INFERENCE IN THE MULTINOMIAL PROBIT MODEL

This research compares several approaches to inference in the multinom ial probit model, based on two Monte Carlo experiments for a seven cho ice model. The methods compared are the simulated maximum likelihood e stimator using the GHK recursive probability simulator, the method of simulated moments estimator using the GHK recursive simulator and kern el-smoothed frequency simulators, and posterior means using a Gibbs sa mpling-data augmentation algorithm. Overall, the Gibbs sampling algori thm has a slight edge, with the relative performance of MSM and SML ba sed on the GHK simulator being difficult to evaluate. The MSM estimato r with the kernel-smoothed frequency simulator is clearly inferior.