We propose using cyclic monotonicity, a convex-analytic property of the random utility choice model, to derive bounds on counterfactual choice probabilities in semiparametric multinomial choice models. These bounds are useful for typical counterfactual exercises in aggregate discrete-choice...

We show how to construct bounds on counterfactual choice probabilities in semiparametric discrete-choice models. Our procedure is based on cyclic monotonicity, a convex-analytic property of the random utility discrete-choice model. These bounds are useful for typical counterfactual exercises in...

We introduce sparse random projection, an important tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. First, the high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. In the second step,...

Using results from convex analysis, we investigate a novel approach to identification and estimation of discrete choice models which we call the “Mass Transport Approach” (MTA). We show that the conditional choice probabilities and the choice specific payoffs in these models are related in...

Endogenous response time data is increasingly becoming available to applied researchers of economic choices. The drift-diffusion model (DDM) was originally developed to jointly explain subjects' choices and response times in laboratory experiments. Here, we adapt the DDM to a field setting to...

We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections....