Counterfactual Evaluation in Semiparametric Multinomial Choice Models

19 Pages Posted: 26 Jul 2017

See all articles by Khai Chiong

Khai Chiong

University of Texas at Dallas - Naveen Jindal School of Management

Yu-Wei Hsieh

University of Southern California - Department of Economics; USC Dornsife Institute for New Economic Thinking

Matthew Shum

California Institute of Technology

Multiple version iconThere are 2 versions of this paper

Date Written: May 30, 2017

Abstract

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

Suggested Citation

Chiong, Khai and Hsieh, Yu-Wei and Shum, Matthew, Counterfactual Evaluation in Semiparametric Multinomial Choice Models (May 30, 2017). USC-INET Research Paper No. 17-20. Available at SSRN: https://ssrn.com/abstract=3006737 or http://dx.doi.org/10.2139/ssrn.3006737

Khai Chiong (Contact Author)

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Yu-Wei Hsieh

University of Southern California - Department of Economics ( email )

3620 South Vermont Ave. Kaprielian (KAP) Hall, 300
Los Angeles, CA 90089
United States

USC Dornsife Institute for New Economic Thinking ( email )

3620 S. Vermont Avenue, KAP 364F
Los Angeles, CA 90089-0253
United States

Matthew Shum

California Institute of Technology ( email )

Pasadena, CA 91125
United States

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