Nonparametric Demand Estimation in Differentiated Products Markets

70 Pages Posted: 9 Mar 2018

See all articles by Giovanni Compiani

Giovanni Compiani

University of California at Berkeley - Haas School of Business

Date Written: January 13, 2018

Abstract

I develop and apply a nonparametric approach to estimate demand in differentiated products markets. Estimating demand flexibly is key to addressing many questions in economics that hinge on the shape - and notably the curvature - of market demand functions. My approach applies to standard discrete choice settings, but accommodates a broader range of consumer behaviors and preferences, including complementarities across goods, consumer inattention, and consumer loss aversion. Further, no distributional assumptions are made on the unobservables and only limited functional form restrictions are imposed. Using California grocery store data, I apply my approach to perform two counterfactual exercises: quantifying the pass-through of a tax, and assessing how much the multi-product nature of sellers contributes to markups. In both cases, I find that estimating demand flexibly has a significant impact on the results relative to a standard random coefficients discrete choice model, and I highlight how the outcomes relate to the estimated shape of the demand functions.

Keywords: Nonparametric demand estimation, Incomplete tax pass-through, Multiproduct fi rm

JEL Classification: L1, L66

Suggested Citation

Compiani, Giovanni, Nonparametric Demand Estimation in Differentiated Products Markets (January 13, 2018). Available at SSRN: https://ssrn.com/abstract=3134152 or http://dx.doi.org/10.2139/ssrn.3134152

Giovanni Compiani (Contact Author)

University of California at Berkeley - Haas School of Business ( email )

2220 Piedmont Ave
Berkeley, CA 94720
United States
(510) 643-4272 (Phone)

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