| By: |
Fosgerau, Mogens;
Hess, Stephane |
| Abstract: |
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 allows 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. |
| Keywords: |
random taste heterogeneity; mixed logit; method of sieves; mixtures of distributions |
| JEL: |
R40 C14 |
| Date: |
2008 |
| URL: |
http://d.repec.org/n?u=RePEc:pra:mprapa:10038&r=dcm |