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Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package

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  • Sarrias, Mauricio
  • Daziano, Ricardo

Abstract

This paper introduces the package gmnl in R for estimation of multinomial logit models with unobserved heterogeneity across individuals for cross-sectional and panel (longitudinal) data. Unobserved heterogeneity is modeled by allowing the parameters to vary randomly over individuals according to a continuous, discrete, or discrete-continuous mixture distribution, which must be chosen a priori by the researcher. In particular, the models supported by gmnl are the multinomial or conditional logit, the mixed multinomial logit, the scale heterogeneity multinomial logit, the generalized multinomial logit, the latent class logit, and the mixed-mixed multinomial logit. These models are estimated using either the maximum likelihood estimator or the maximum simulated likelihood estimator. This article describes and illustrates with real databases all functionalities of gmnl, including the derivation of individual conditional estimates of both the random parameters and willingness-to-pay measures.

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  • Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    • Handle: RePEc:jss:jstsof:v:079:i02
    DOI: http://hdl.handle.net/10.18637/jss.v079.i02
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    1. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    4. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
    5. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 90(4), pages 1-9, January.
    6. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    7. Garrett Sonnier & Andrew Ainslie & Thomas Otter, 2007. "Heterogeneity distributions of willingness-to-pay in choice models," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 313-331, September.
    8. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    9. Daziano, Ricardo A. & Achtnicht, Martin, 2014. "Accounting for uncertainty in willingness to pay for environmental benefits," Energy Economics, Elsevier, vol. 44(C), pages 166-177.
    10. Riccardo Scarpa & Mara Thiene, 2005. "Destination Choice Models for Rock Climbing in the Northeastern Alps: A Latent-Class Approach Based on Intensity of Preferences," Land Economics, University of Wisconsin Press, vol. 81(3).
    11. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    12. Peter Boxall & Wiktor Adamowicz, 2002. "Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(4), pages 421-446, December.
    13. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    14. Hasan, Asad & Wang, Zhiyu & Mahani, Alireza S., 2016. "Fast Estimation of Multinomial Logit Models: R Package mnlogit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i03).
    15. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    16. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    17. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    18. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    19. Zeileis, Achim & Croissant, Yves, 2010. "Extended Model Formulas in R: Multiple Parts and Multiple Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i01).
    20. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    21. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    22. Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
    23. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    24. Gourieroux, Christian & Monfort, Alain, 1997. "Simulation-based Econometric Methods," OUP Catalogue, Oxford University Press, number 9780198774754.
    25. Riccardo Scarpa & Mara Thiene & Kenneth Train, 2008. "Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 994-1010.
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