Economics at your fingertips  

Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions

Mazen Danaf, Bilge Atasoy and Moshe Ben-Akiva

Journal of choice modelling, 2020, vol. 35, issue C

Abstract: Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively). However, the underlying model relies on strong assumptions on the inter- and intra-consumer mixing distributions; these distributions are assumed to be normal (or log-normal), and the intra-consumer covariance matrix is assumed to be the same for all individuals. This paper presents a latent class extension to the model and the estimator proposed by Becker et al. (2018) to account for flexible, semi-parametric mixing distributions. This relaxes the normality assumptions and allows different individuals to have different intra-consumer covariance matrices. The proposed model and the HB estimator are validated using real and synthetic data sets, and the models are evaluated using goodness-of-fit statistics and out-of-sample validation. Our results show that when the data comes from two or more distinct classes (with different population means and inter- and intra-consumer covariance matrices), this model results in a better fit and predictions compared to the single class model.

Keywords: Logit mixture; Bayesian estimation; Latent class.; Intra-consumer heterogeneity; Flexible mixing distributions (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.jocm.2019.100188

Access Statistics for this article

Journal of choice modelling is currently edited by S. Hess and J.M. Rose

More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2021-06-30
Handle: RePEc:eee:eejocm:v:35:y:2020:i:c:s1755534519300934