| Abstract: |
We present a mixed multinomial logit (MNL) model, which leverages the
truncated stick-breaking process representation of the Dirichlet process as a
flexible nonparametric mixing distribution. The proposed model is a Dirichlet
process mixture model and accommodates discrete representations of
heterogeneity, like a latent class MNL model. Yet, unlike a latent class MNL
model, the proposed discrete choice model does not require the analyst to fix
the number of mixture components prior to estimation, as the complexity of the
discrete mixing distribution is inferred from the evidence. For posterior
inference in the proposed Dirichlet process mixture model of discrete choice,
we derive an expectation maximisation algorithm. In a simulation study, we
demonstrate that the proposed model framework can flexibly capture
differently-shaped taste parameter distributions. Furthermore, we empirically
validate the model framework in a case study on motorists' route choice
preferences and find that the proposed Dirichlet process mixture model of
discrete choice outperforms a latent class MNL model and mixed MNL models with
common parametric mixing distributions in terms of both in-sample fit and
out-of-sample predictive ability. Compared to extant modelling approaches, the
proposed discrete choice model substantially abbreviates specification
searches, as it relies on less restrictive parametric assumptions and does not
require the analyst to specify the complexity of the discrete mixing
distribution prior to estimation. |