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Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers

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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File URL: http://cowles.yale.edu/sites/default/files/files/pub/d17/d1718.pdf
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Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1718.

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Length: 42 pages
Date of creation: Sep 2009
Date of revision: Mar 2010
Handle: RePEc:cwl:cwldpp:1718
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Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Web page: http://cowles.yale.edu/

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Keywords: Nonparametric identification; Discrete choice demand; Differentiated products;

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Find related papers by JEL classification:
  • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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