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Bayesian analysis of nested logit model by Markov chain Monte Carlo

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  • Lahiri, Kajal
  • Gao, Jian

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.
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  • Lahiri, Kajal & Gao, Jian, 2002. "Bayesian analysis of nested logit model by Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 111(1), pages 103-133, November.
    • Handle: RePEc:eee:econom:v:111:y:2002:i:1:p:103-133
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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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