Masking Identification of Discrete Choice Models Under Simulation Methods

Journal of Econometrics, Vol. 141, December 2007

36 Pages Posted: 3 Sep 2008

See all articles by Lesley Chiou

Lesley Chiou

Occidental College - Department of Economics

Joan L. Walker

University of California, Berkeley - Department of Civil and Environmental Engineering

Date Written: December 2007

Abstract

We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact, are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation, we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.

Keywords: simulation methods, discrete choice, identification

JEL Classification: C15, C25

Suggested Citation

Chiou, Lesley and Walker, Joan L., Masking Identification of Discrete Choice Models Under Simulation Methods (December 2007). Journal of Econometrics, Vol. 141, December 2007, Available at SSRN: https://ssrn.com/abstract=1262376

Lesley Chiou (Contact Author)

Occidental College - Department of Economics ( email )

1600 Campus Road
Los Angeles, CA 90041
United States

HOME PAGE: http://www.faculty.oxy.edu/lchiou

Joan L. Walker

University of California, Berkeley - Department of Civil and Environmental Engineering ( email )

Berkeley, CA
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
134
Abstract Views
1,092
rank
265,405
PlumX Metrics