Nonparametric Estimation of Semiparametric Transformation Models

Jean-Pierre Florens, Senay Sokullu

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
456 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)839-873
Number of pages35
JournalEconometric Theory
Issue number4
Early online date6 Jun 2016
Publication statusPublished - Aug 2017

Structured keywords

  • ECON Econometrics


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


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