Identification via completeness for discrete covariates and orthogonal polynomials

Yevgeniy Kovchegov () and Nese Yildiz ()
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Yevgeniy Kovchegov: University of Rochester, Department of Mathematics
Nese Yildiz: Oregon State University, Department of Economics

Koç University-TUSIAD Economic Research Forum Working Papers from Koc University-TUSIAD Economic Research Forum

Abstract: We solve a class of identification problems for nonparametric and semiparametric models when the endogenous covariate is discrete with unbounded support. Then we proceed with an approach that resolves a polynomial basis problem for the above class of discrete distributions, and for the distributions given in the sufficient condition for completeness in Newey and Powell (2003). Thus, in addition to extending the set of econometric models for which nonparametric or semiparametric identification of structural functions is guaranteed to hold, our approach provides a natural way of estimating these functions. Finally, we extend our polynomial basis approach to Pearson-like and Ord-like families of distributions.

Keywords: nonparametric methods; identification; instrumental variables. (search for similar items in EconPapers)
Pages: 15 pages
Date: 2012-01
New Economics Papers: this item is included in nep-ecm
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