查看文章

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 Revelt and Train (2000) 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 …