  This study proposes a mixed logit model with multivariate nonparametric
finite mixture distributions. The support of the distribution is specified as a
high-dimensional grid over the coefficient space, with equal or unequal
intervals between successive points along the same dimension; the location of
each point on the grid and the probability mass at that point are model
parameters that need to be estimated. The framework does not require the
analyst to specify the shape of the distribution prior to model estimation, but
can approximate any multivariate probability distribution function to any
arbitrary degree of accuracy. The grid with unequal intervals, in particular,
offers greater flexibility than existing multivariate nonparametric
specifications, while requiring the estimation of a small number of additional
parameters. An expectation maximization algorithm is developed for the
estimation of these models. Multiple synthetic datasets and a case study on
travel mode choice behavior are used to demonstrate the value of the model
framework and estimation algorithm. Compared to extant models that incorporate
random taste heterogeneity through continuous mixture distributions, the
proposed model provides better out-of-sample predictive ability. Findings
reveal significant differences in willingness to pay measures between the
proposed model and extant specifications. The case study further demonstrates
the ability of the proposed model to endogenously recover patterns of attribute
non-attendance and choice set formation.
