Counterfactual Estimation in Semiparametric Discrete-Choice Models

19 Pages Posted: 4 Jun 2017 Last revised: 8 Jun 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: June 2, 2017

Abstract

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 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 Discrete-Choice Models; Counterfactuals; Convex Analysis; Cyclic Monotonicity; Linear Programming

JEL Classification: C14, C25, C53

Suggested Citation

Chiong, Khai and Hsieh, Yu-Wei and Shum, Matthew, Counterfactual Estimation in Semiparametric Discrete-Choice Models (June 2, 2017). Available at SSRN: https://ssrn.com/abstract=2979446 or http://dx.doi.org/10.2139/ssrn.2979446

Khai Chiong

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 (Contact Author)

California Institute of Technology ( email )

Pasadena, CA 91125
United States

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