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Estimating Multinomial Choice Models using Cyclic Monotonicity

Shi, Xiaoxia and Shum, Matthew and Song, Wei (2014) Estimating Multinomial Choice Models using Cyclic Monotonicity. Social Science Working Paper, 1397. California Institute of Technology , Pasadena, CA. (Unpublished)

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This paper proposes a new identification and estimation approach to semi-parametric multinomial choice models that easily applies to not only cross-sectional settings but also panel data settings with unobservable fixed effects. Our approach is based on cyclic monotonicity, which is a defining feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requiring any shape restriction for the distribution of the random utility shocks. These inequalities point identify model parameters under straightforward assumptions on the covariates. We propose a consistent estimator based on these inequalities, and apply it to a panel data set to study the determinants of the demand of bathroom tissue.

Item Type:Report or Paper (Working Paper)
Shum, Matthew0000-0002-6262-915X
Additional Information:October 2014. Acknowledgment: We thank Bruce Hansen, Federico Echenique, Jack Porter, and seminar audiences at Northwestern, NYU, and UC Riverside for useful comments. Jun Zhang provided excellent research assistance. Xiaoxia Shi acknowledges the financial support of the Wisconsin Alumni Research Foundation via the Graduate School Fall Competition Grant.
Group:Social Science Working Papers
Funding AgencyGrant Number
Wisconsin Alumni Research FoundationUNSPECIFIED
Subject Keywords:Cyclic Monotonicity, Multinomial Choice, Panel Data, Fixed Effects
Series Name:Social Science Working Paper
Issue or Number:1397
Classification Code:JEL: C14, C25
Record Number:CaltechAUTHORS:20160329-095352718
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65733
Deposited On:30 Mar 2016 23:10
Last Modified:03 Oct 2019 09:49

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