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Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo

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  • Kajal Lahiri
  • Jian Gao

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.

Suggested Citation

  • Kajal Lahiri & Jian Gao, 2001. "Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo," Discussion Papers 01-14, University at Albany, SUNY, Department of Economics.
    • Handle: RePEc:nya:albaec:01-14
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    1. Method of the month: custom likelihoods with Stan
      by Sam Watson in The Academic Health Economists' Blog on 2018-05-18 06:00:27

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    7. Sergio Aquino de Souza, 2008. "Combining Prior Information and Data to Uncover the Parameters from the Random Coefficient Discrete? Choice Demand Model," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807211342080, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
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