Counterfactual Evaluation in Semiparametric Multinomial Choice Models
19 Pages Posted: 26 Jul 2017
Date Written: May 30, 2017
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 demand models. In our semiparametric approach, we do not specify the parametric distribution for the utility shocks, thus accommodating a wide variety of substitution patterns among alternatives. Computation of the counterfactual bounds is a tractable linear programming problem. We illustrate our approach in a series of Monte Carlo simulations and an empirical application using scanner data.
Keywords: Semiparametric Multinomial Choice Models; Counterfactual prediction; Convex Analysis, Cyclic Monotonicity; Linear Programming
JEL Classification: C14, C25, C53
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