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Identification via completeness for discrete covariates and orthogonal polynomials

Author

Listed:
  • Yevgeniy Kovchegov

    (University of Rochester, Department of Mathematics)

  • Nese Yildiz

    (Oregon State University, Department of Economics)

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.

Suggested Citation

  • 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

    as
    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.
    2. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2007. "Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves," Econometrica, Econometric Society, vol. 75(6), pages 1613-1669, November.
    3. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
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