This paper reports the findings of a systematic study using Monte Carlo experiments and a real dataset
aimed at comparing the performance of various ways of specifying random taste heterogeneity in a
discrete choice model. Specifically, the analysis compares the performance of two recent advanced
approaches against a background of four commonly used continuous distribution functions. The first of
these two approaches improves on the flexibility of a base distribution by adding in a series
approximation using Legendre polynomials. The second approach uses a discrete mixture of multiple
continuous distributions. Both approaches allow the researcher to increase the number of parameters as
desired. The paper provides a range of evidence on the ability of the various approaches to recover
various distributions from data. The two advanced approaches are comparable in terms of the likelihoods
achieved, but each has its own advantages and disadvantages.
