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Nonparametric Identification of Discrete Choice Models

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Abstract

In this paper we give simple proofs of identification results in discrete choice models for the case where neither the deterministic part nor the distribution function of the random parts of the utility function is specified parametrically. The regularity conditions imposed are standard, but differ from conditions applied by other researchers, such as Matzkin (1992, 1993).

Suggested Citation

  • John K. Dagsvik, 1998. "Nonparametric Identification of Discrete Choice Models," Discussion Papers 222, Statistics Norway, Research Department.
    • Handle: RePEc:ssb:dispap:222
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    References listed on IDEAS

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    1. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
    2. Thompson, T.S., 1989. "Identification Of Semiparametric Discrete Choice Models," Papers 249, Minnesota - Center for Economic Research.
    3. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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