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Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo

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Author Info
Kajal Lahiri
Jian Gao

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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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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number 01-14.

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Date of creation: 2001
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Handle: RePEc:nya:albaec:01-14

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
Phone: (518) 442-4735
Fax: (518) 442-4736

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Web: http://www.albany.edu/economics/research/workingp/index.html

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Keywords: Discrete Choice; Random Utility Maximization; MCMC; Mixing Speed.;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Herriges, Joseph A. & Kling, Catherine L., 1996. "Testing the consistency of nested logit models with utility maximization," Economics Letters, Elsevier, vol. 50(1), pages 33-39, January. [Downloadable!] (restricted)
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  2. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Matthews, Peter, 1993. "A slowly mixing Markov chain with implications for Gibbs sampling," Statistics & Probability Letters, Elsevier, vol. 17(3), pages 231-236, June. [Downloadable!] (restricted)
  5. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November. [Downloadable!] (restricted)
  6. Cameron, Trudy Ann, 1985. "A Nested Logit Model of Energy Conservation Activity by Owners of Existing Single Family Dwellings," The Review of Economics and Statistics, MIT Press, vol. 67(2), pages 205-11, May. [Downloadable!] (restricted)
  7. Daniel McFadden, 1975. "The Revealed Preferences of a Government Bureaucracy: Theory," Bell Journal of Economics, The RAND Corporation, vol. 6(2), pages 401-416, Autumn. [Downloadable!] (restricted)
  8. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," Journal of Business, University of Chicago Press, vol. 53(3), pages S13-29, July. [Downloadable!] (restricted)
  9. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August. [Downloadable!]
  10. Brownstone, David & Small, Kenneth A, 1989. "Efficient Estimation of Nested Logit Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 67-74, January.
  11. Geweke, John, 1994. "Priors for Macroeconomic Time Series and Their Application," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 609-632, August. [Downloadable!]
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  1. María José Gil-Moltó & Arne Risa Hole, 2003. "Tests for the consistency of three-level nested logit models with utility maximization," Econometrics 0312001, EconWPA, revised 08 Jan 2004. [Downloadable!]
    Other versions:
  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]. [Downloadable!]
  3. 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. [Downloadable!] (restricted)
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