Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo

Posted: 11 Oct 2002

See all articles by Kajal Lahiri

Kajal Lahiri

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics

Jian Gao

SUNY at Albany, College of Arts and Sciences, Economics

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Abstract

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving 3-level structures. The first example involves Social Security's disability determination process (Soc. Security Bull. 58 (1995)). The second one is taken from Amemiya and Shimono's (Econ. Stud. Q. 40 (1989)) model of labor supply behavior of the aged. We applied a combination of various convergence criteria to ensure that the chain has converged to its target distribution.

Keywords: Discrete Choice, Random utility maximization, MCMC, Mixing speed

JEL Classification: C11, C25, H55, I12, J14

Suggested Citation

Lahiri, Kajal and Gao, Jian, Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo. Journal of Econometrics, Vol. 11, No. 1, Pp. 103-133, November 2002. Available at SSRN: https://ssrn.com/abstract=322060

Kajal Lahiri (Contact Author)

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics ( email )

Department of Economics
1400 Washington Avenue
Albany, NY 12222
United States
518-442 4758 (Phone)
518-442 4736 (Fax)

HOME PAGE: http://www.albany.edu/~klahiri

Jian Gao

SUNY at Albany, College of Arts and Sciences, Economics ( email )

1400 Washington Avenue
Albany, NY 12222
United States

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