Individual-Specific Point and Interval Conditional Estimates of Latent Class Logit Parameters

22 Pages Posted: 7 Nov 2017

See all articles by Mauricio Sarrias

Mauricio Sarrias

Universidad Católica Del Norte

Ricardo Daziano

Cornell University

Date Written: October 27, 2017

Abstract

Within the realm of logit-type random parameter models to address unobserved heterogeneity in preferences there are two dominant approaches: the mixed logit model, which assumes parametric and continuous heterogeneity distributions, and the latent class logit model, which is a discrete and semiparametric counterpart of mixed logit. In addition to offer flexibility benefits, random parameter models allow researchers to make conditional (posterior) inference on preference parameters at the individual-specific level. In this paper we extend the individual-specific experimental approach, that was conducted by for the continuous heterogeneity distributions of a mixed logit, to the discrete case of the latent class logit model. Our Monte Carlo study results confirm the expectation that for a given number of individuals, the density of the conditional means converges to the conditional population as the number of choice situations increases. We also add to the analysis the behavior of interval estimates using two methods for the derivation of standard errors of the individual-specific estimates. In general, as we have more information of the choices made by the individuals, we are in better shape to identify individual-specific preferences. Our main conclusion is that accurate individual-specific estimation is possible – including correct assignment to classes, but a large number of choice situations is needed to correctly approximate the true underlying distribution.

Keywords: discrete choice, discrete heterogeneity, random parameters, standard errors

JEL Classification: C25, C53

Suggested Citation

Sarrias, Mauricio and Daziano, Ricardo, Individual-Specific Point and Interval Conditional Estimates of Latent Class Logit Parameters (October 27, 2017). Available at SSRN: https://ssrn.com/abstract=3065365 or http://dx.doi.org/10.2139/ssrn.3065365

Mauricio Sarrias

Universidad Católica Del Norte

Avenida Angamos 0610
Antofagasta, II Region 1270709
Chile

Ricardo Daziano (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States

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