Bayesian analysis of nested logit model by Markov chain Monte Carlo

被引：15

作者：

Lahiri, K ^{[1
]}

Gao, J ^{[1
]}

机构：

[1] SUNY Albany, Dept Econ, Albany, NY 12222 USA

来源：

JOURNAL OF ECONOMETRICS | 2002年 / 111卷 / 01期

关键词：

discrete choice; random utility maximization; MCMC; mixing speed;

D O I：

10.1016/S0304-4076(02)00125-2

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We develop a Markov chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametetization 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) 3). The second one is taken from Amemiya and Shimono's (Econ. Stud. Q. 40 (1989) 14) 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. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：103 / 133

页数：31

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