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Nonparametric Estimation of Semiparametric Transformation Models

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)839-873
Number of pages35
JournalEconometric Theory
Volume33
Issue number4
Early online date6 Jun 2016
DOIs
DateAccepted/In press - 8 May 2016
DateE-pub ahead of print - 6 Jun 2016
DatePublished (current) - Aug 2017

Abstract

In this paper we develop a nonparametric estimation technique for semiparametric transformation models of the form: H(Y) =℘(Z) + X'β + U where H;℘ are unknown functions, is an unknown nite-dimensional parameter vector and the variables (Y;Z) are endogenous. Identification of the model and asymptotic properties of the estimator are analyzed under the mean independence assumption between the error term and the instruments. We show that the estimators are consistent, and a √N-convergence rate and asymptotic normality for β can be attained. The simulations demonstrate that our nonparametric estimates fit the data well.

    Research areas

  • Nonparametric IV Regression, Inverse problems, Tikhonov Regularization, Regularization Parameter

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Cambridge University Press at http://dx.doi.org/10.1017/S0266466616000190. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 794 KB, PDF document

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