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Semiparametric Estimation of Monotonic and Concave Utility Functions: The Discrete Choice Case

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Abstract

This paper develops a semiparametric method for estimating the nonrandom part V(.) of a random utility function U(v, omega) - V(v) + e(omega) from data on discrete choice behavior. Here v and omega are, respectively, vectors of observable and unobservable attributes of an alternative, and e(omega) is the random part of the utility for that alternative. The method is semiparametric because it assumes that the distribution of the random parts is know up to a finite-dimensional parameter theta, while not requiring specification of a parametric form for V( ). The nonstochastic part V( ) of the utility function U( ) is assumed to be Lipschitzian and to possess a set of properties, typically assumed for utility functions. The estimator of the pair (V,theta) is shown to be strongly consistent.

Suggested Citation

  • Rosa L. Matzkin, 1987. "Semiparametric Estimation of Monotonic and Concave Utility Functions: The Discrete Choice Case," Cowles Foundation Discussion Papers 830, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:830
    Note: CFP 795.
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    1. Sidney N. Afriat, 1972. "The Theory of International Comparisons of Real Income and Prices," NBER Chapters, in: International Comparisons of Prices and Output, pages 13-84, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Rosa L. Matzkin, 1989. "A Nonparametric Maximum Rank Correlation Estimator," Cowles Foundation Discussion Papers 918, Cowles Foundation for Research in Economics, Yale University.

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