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NONPARAMETRIC ESTIMATION OF SEMIPARAMETRIC TRANSFORMATION MODELS

Published online by Cambridge University Press:  06 June 2016

Jean-Pierre Florens  and
Senay Sokullu
Jean-Pierre Florens
Affiliation:
Toulouse School of Economics
Senay Sokullu*
Affiliation:
University of Bristol
*
*Address correspondence to Senay Sokullu, Department of Economics, University of Bristol, 8 Woodland Road, Bristol BS8 1TN, United Kingdom; e-mail: Senay.Sokullu@bristol.ac.uk.
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Abstract

In this paper we develop a nonparametric estimation technique for semiparametric transformation models of the form: H (Y) = φ(Z) + X′β + U where H,φ are unknown functions, β is an unknown finite-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 $\sqrt N$ -convergence rate and asymptotic normality for $\hat \beta$ can be attained. The simulations demonstrate that our nonparametric estimates fit the data well.

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ARTICLES
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Econometric Theory , Volume 33 , Issue 4 , August 2017 , pp. 839 - 873
Copyright
Copyright © Cambridge University Press 2016 

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