Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo

Kajal Lahiri and Jian Gao

Discussion Papers from University at Albany, SUNY, Department of Economics

Abstract: We develop a Markov Chain Monte Carlo (MCMC) algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparameterization 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, Lahiri et al. (1995). The second one is taken from Amemiya and Shimono's (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. (search for similar items in EconPapers)
Date: 2001
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