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Identification via completeness for discrete covariates and orthogonal polynomials


  • Yevgeniy Kovchegov

    () (University of Rochester, Department of Mathematics)

  • Nese Yildiz

    () (Oregon State University, Department of Economics)


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.

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  • Yevgeniy Kovchegov & Nese Yildiz, 2012. "Identification via completeness for discrete covariates and orthogonal polynomials," Koç University-TUSIAD Economic Research Forum Working Papers 1203, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1203

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    References listed on IDEAS

    1. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2003. "Nonparametric IV estimation of shape-invariant Engel curves," CeMMAP working papers CWP15/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    nonparametric methods; identification; instrumental variables.;

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