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Methods for Nonparametric and Semiparametric Regressions with Endogeneity: a Gentle Guide

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    This paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric instrumental variables regression (NPIV), nonparametric quantile IV regression and many more semi-nonparametric structural models. Asymptotic properties of the sieve estimators and the sieve Wald, quasi-likelihood ratio (QLR) hypothesis tests of functionals with nonparametric endogeneity are presented. For sieve NPIV estimation, the rate-adaptive data-driven choices of sieve regularization parameters and the sieve score bootstrap uniform confidence bands are described. Finally, simple sieve variance estimation and over-identification test for semiparametric two-step GMM are reviewed. Monte Carlo examples are included.

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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2032.pdf
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    Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 2032.

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    Length: 71 pages
    Date of creation: Mar 2016
    Handle: RePEc:cwl:cwldpp:2032
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    Yale University, Box 208281, New Haven, CT 06520-8281 USA

    Phone: (203) 432-3702
    Fax: (203) 432-6167
    Web page: http://cowles.yale.edu/

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    Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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