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 [Brathwaite] greatly expand the discrete choice modelers’ assessment toolkit by introducing model checking procedures based on graphical displays of predictive simulations. . . . a general and ‘semi-automatic’ algorithm for checking discrete choice models via predictive simulations. . . .
He frames model checking in terms of “underfitting,” a connection I’ve never seen before but which makes sense. To the extent that there are features in your data that are not captured in your model—more precisely, features that don’t show up, even in many different posterior predictive simulations from your fitted model—then, yes, the model is underfitting the data. Good point.