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Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers

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Author Info
Steven T. Berry
Philip A. Haile

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Abstract

We consider identification of nonparametric random utility models of multinomial choice using "micro data," i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. However, the model is nonparametric and distribution free. It allows choice-specific unobservables, endogenous choice characteristics, unknown heteroskedasticity, and high-dimensional correlated taste shocks. Under standard "large support" and instrumental variables assumptions, we show identifiability of the random utility model. We demonstrate robustness of these results to relaxation of the large support condition and show that when it is replaced with a weaker "common choice probability" condition, the demand structure is still identified. We show that key maintained hypotheses are testable.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15276.

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Date of creation: Aug 2009
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Handle: RePEc:nbr:nberwo:15276

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C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Christopher T. Conlon & Julie Holland Mortimer, 2008. "Demand Estimation Under Incomplete Product Availability," NBER Working Papers 14315, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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