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Nonparametric Estimation of Nonadditive Random Functions

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  • Rosa L. Matzkin

    (Northwestern University & Universidad de San Andres)

Abstract

We present estimators for nonparametric functions that depend on unobservable random variables in nonadditive ways. The distributions of the unobservable random terms are assumed to be unknown. We show how properties that may be implied by economic theory, such as monotonicity, homogeneity of degree one, and separability can be used to identify the unknown, nonparametric functions and distributions. We also present convenient normalizations, to use when the properties of the functions are unknown. The estimators for the nonparametric distributions and for the nonparametric functions and their derivatives are shown to be consistent and asymptotically normal. The results of a limited simulation study are presented.

Suggested Citation

  • Rosa L. Matzkin, 1999. "Nonparametric Estimation of Nonadditive Random Functions," Working Papers 38, Universidad de San Andres, Departamento de Economia, revised Sep 2001.
    • Handle: RePEc:sad:wpaper:38
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    File URL: https://webacademicos.udesa.edu.ar/pub/econ/doc38.pdf
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    References listed on IDEAS

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