| Abstract: |
The mixed logit is a framework for incorporating unobserved heterogeneity in
discrete choice models in a general way. These models are difficult to
estimate because they result in a complicated incomplete data likelihood. This
paper proposes a new approach for estimating mixed logit models. The estimator
is easily implemented as iteratively re-weighted least squares: the well known
solution for complete data likelihood logits. The main benefit of this
approach is that it requires drastically fewer evaluations of the simulated
likelihood function, making it significantly faster than conventional methods
that rely on numerically approximating the gradient. The method is rooted in a
generalized expectation and maximization (GEM) algorithm, so it is
asymptotically consistent, efficient, and globally convergent. |