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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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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 111 (2002)
Issue (Month): 1 (November)
Pages: 103-133

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Handle: RePEc:eee:econom:v:111:y:2002:i:1:p:103-133
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  1. Daniel McFadden, 1977. "Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments," Cowles Foundation Discussion Papers 474, Cowles Foundation for Research in Economics, Yale University.
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  17. Falaris, Evangelos M, 1987. "A Nested Logit Migration Model with Selectivity," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(2), pages 429-43, June.
  18. Borsch-Supan, Axel, 1990. "On the compatibility of nested logit models with utility maximization," Journal of Econometrics, Elsevier, vol. 43(3), pages 373-388, March.
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  23. Jianting Hu & Kajal Lahiri & Denton R. Vaughan & Bernard Wixon, 2001. "A Structural Model Of Social Security'S Disability Determination Process," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 348-361, May.
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Cited by:

  1. Gary Koop, 2004. "Modeling the Evolution of Distributions: An Application to Major League Baseball," ESE Discussion Papers 71, Edinburgh School of Economics, University of Edinburgh.
  2. 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 Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  3. Verlinda, Jeremy A., 2005. "A Bayesian analysis of tree structure specification in nested logit models," Economics Letters, Elsevier, vol. 87(1), pages 67-73, April.
  4. Ricardo A. Daziano & Luis Miranda-Moreno & Shahram Heydari, 2013. "Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice," Transport Reviews, Taylor & Francis Journals, vol. 33(5), pages 570-592, September.
  5. Gil-Molto, Maria Jose & Hole, Arne Risa, 2004. "Tests for the consistency of three-level nested logit models with utility maximization," Economics Letters, Elsevier, vol. 85(1), pages 133-137, October.
  6. Sergio Aquino de Souza, 2011. "A Simplified Mixed Logit Demand Model with an Application to the Simulation of Entry," Working Papers 04-2011, Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto.
  7. Haijime Katayama & Shihua Lu & James Tybout, 2003. "Why Plant-Level Productivity Studies are Often Misleading, and an Alternative Approach to Interference," NBER Working Papers 9617, National Bureau of Economic Research, Inc.
  8. Katayama, Hajime & Lu, Shihua & Tybout, James R., 2009. "Firm-level productivity studies: Illusions and a solution," International Journal of Industrial Organization, Elsevier, vol. 27(3), pages 403-413, May.

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