Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers
CFDP Revision Date
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.
Berry, Steven T. and Haile, Philip A., "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers" (2009). Cowles Foundation Discussion Papers. 2037.