Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package | Sarrias | Journal of Statistical Software
Authors: Mauricio Sarrias, Ricardo Daziano
Title: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package
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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Paper: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package     Download PDF (Downloads: 525)
Supplements:
gmnl_1.1-3.tar.gz: R source package Download (Downloads: 24; 33KB)
v79i02.R: R replication code Download (Downloads: 39; 9KB)

DOI: 10.18637/jss.v079.i02

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