Nonparametric Identification Discrete

Nonparametric Identification Discrete - NBER WORKING PAPER...

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Unformatted text preview: NBER WORKING PAPER SERIES NONPARAMETRIC IDENTIFICATION OF MULTINOMIAL CHOICE DEMAND MODELS WITH HETEROGENEOUS CONSUMERS Steven T. Berry Philip A. Haile Working Paper 15276 http://www.nber.org/papers/w15276 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 August 2009 We have had helpful conversations on this topic with Liran Einav, Jin Hahn, Hide Ichimura, Jon Levin, Rosa Matzkin and Yuichi Kitamura. We also received useful comments from Sunyoung Park and participants in several conferences and seminars. Financial support from the NSF is gratefully acknowledged. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. 2009 by Steven T. Berry and Philip A. Haile. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including notice, is given to the source. Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers Steven T. Berry and Philip A. Haile NBER Working Paper No. 15276 August 2009, Revised March 2010 JEL No. C35 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. Steven T. Berry Yale University Department of Economics Box 208264 37 Hillhouse Avenue New Haven, CT 06520-8264 and NBER steven.berry@yale.edu Philip A. Haile Department of Economics Yale University 37 Hillhouse Avenue P.O. Box 208264 New Haven, CT 06520 and NBER philip.haile@yale.edu 1 Introduction We consider identi&cation of nonparametric random utility models of multinomial choice using micro data, i.e., observation of the characteristics and choices of individual con- sumers. Our model of preferences nests random coe cients 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-speci&c unobservables, endogenous choice characteristics, unknown heteroskedasticity, and high-dimensional corre- lated taste shocks. Under standard large supportand instrumental variables assumptions,lated taste shocks....
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Nonparametric Identification Discrete - NBER WORKING PAPER...

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