The vast majority of discrete choice modelling (DCM) applications are now estimated on Stated Preference (SP) data, including but not limited to the field of transport research. In SP data, each respondent is faced with multiple choice situations, and recognising this repeated choice nature of the data is a crucial modelling issue. With the increasing popularity of the Mixed Multinomial Logit (...

We present a structural model of political advertising in equilibrium. Candidates choose advertising across media markets in order to maximize the probability of winning the national election. The voter model takes the form of an aggregate random coefficients discrete choice model in which advertising affects a voter’s incentive to vote for either candidate or not to vote at all. We estimate th...

We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...

An important goal of empirical demand analysis is choice and welfare prediction on counterfactual budget sets arising from potential policy interventions. Such predictions are more credible when made without arbitrary functional?form/distributional assumptions, instead based solely economic rationality, that is, consistent with utility maximization by a heterogeneous population. This paper inve...