  Typically, discrete choice modelers develop ever-more advanced models and
estimation methods. Compared to the impressive progress in model development
and estimation, model-checking techniques have lagged behind. Often, choice
modelers use only crude methods to assess how well an estimated model
represents reality. Such methods usually stop at checking parameter signs,
model elasticities, and ratios of model coefficients. In this paper, I greatly
expand the discrete choice modelers' assessment toolkit by introducing model
checking procedures based on graphical displays of predictive simulations.
  Overall, my contributions are as follows. Methodologically, I introduce a
general and 'semi-automatic' algorithm for checking discrete choice models via
predictive simulations. By combining new graphical displays with existing
plots, I introduce methods for checking one's data against one's model in terms
of the model's predicted distributions of choices (P(Y)), choices given
explanatory variables (P(Y|X)), and explanatory variables given choices
(P(X|Y)). Empirically, I demonstrate my proposed methods by checking the models
from Brownstone and Train (1998). Through this case study, I show that my
proposed methods can point out lack-of-model-fit in one's models and suggest
concrete model improvements that substantively change the results of one's
policy analysis. Moreover, the case study highlights a practical trade-off
between precision and robustness in model checking.
