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Least Concavity and the Distribution-Free Estimation of Non-Parametric Concave Functions

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Abstract

This paper studies the estimation of fully nonparametric models in which we can not identify the values of a symmetric function that we seek to estimate. I develop a method of consistently estimating a representative of a concave and monotone nonparametric systematic function. This representative possesses the same isovalue sets as the systematic function. The method proceeds by characterizing each set of observationally equivalent concave functions by a unique "least concave" representative. The least concave representative of the equivalence class to which the systematic function belongs is estimated by maximizing a criterion function over a compact set of least concave functions. I develop a computational technique to evaluate the values, at the observed points, and the gradients, at every point and up to a constant, of this least concave estimator. The paper includes a detailed description of how the method can be used to estimate three popular microeconometric models.

Suggested Citation

  • Rosa L. Matzkin, 1990. "Least Concavity and the Distribution-Free Estimation of Non-Parametric Concave Functions," Cowles Foundation Discussion Papers 958, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:958
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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d09/d0958.pdf
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    1. Kannai, Yakar, 1977. "Concavifiability and constructions of concave utility functions," Journal of Mathematical Economics, Elsevier, vol. 4(1), pages 1-56, March.
    2. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    3. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-782, May.
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    Cited by:

    1. Susan Athey & Scott Stern, 1998. "An Empirical Framework for Testing Theories About Complimentarity in Organizational Design," NBER Working Papers 6600, National Bureau of Economic Research, Inc.
    2. Taber, Christopher R., 2000. "Semiparametric identification and heterogeneity in discrete choice dynamic programming models," Journal of Econometrics, Elsevier, vol. 96(2), pages 201-229, June.
    3. Athey, Susan. & Stern, Scott, 1969-, 1998. "An empirical framework for testing theories about complementarity in orgaziational design," Working papers WP 4022-98., Massachusetts Institute of Technology (MIT), Sloan School of Management.

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