Alternative computational approaches to inference in the multinomial probit model
Abstract: This research compares several approaches to inference in the multinomial probit model, based on Monte-Carlo results for a seven choice model. The experiment compares the simulated maximum likelihood estimator using the GHK recursive probability simulator, the method of simulated moments estimator using the GHK recursive simulator and kernel-smoothed frequency simulators, and posterior means using a Gibbs sampling-data augmentation algorithm. Each estimator is applied in nine different models, which have from 1 to 40 free parameters. The performance of all estimators is found to be satisfactory. However, the results indicate that the method of simulated moments estimator with the kernel-smoothed frequency simulator does not perform quite as well as the other three methods. Among those three, the Gibbs sampling-data augmentation algorithm appears to have a slight overall edge, with the relative performance of MSM and SML based on the GHK simulator difficult to determine.
Keywords: Econometric models;
File(s): File format is application/pdf http://www.minneapolisfed.org/research/common/pub_detail.cfm?pb_autonum_id=451
Provider: Federal Reserve Bank of Minneapolis
Part of Series: Staff Report
Publication Date: 1994