Nonparametric Estimation of Nonadditive Random Functions

Rosa Matzkin

No 38, Working Papers from Universidad de San Andres, Departamento de Economia

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.

Keywords: nonparametric estimation; nonadditive random term; nonseparable models; shape restrictions; conditional distributions; kernel estimators (search for similar items in EconPapers)
Pages: 60 pages
Date: 1999-04, Revised 2001-09
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https://webacademicos.udesa.edu.ar/pub/econ/doc38.pdf Revised version, 2001 (application/pdf)

Related works:
Journal Article: Nonparametric Estimation of Nonadditive Random Functions (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:sad:wpaper:38

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