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Additive Nonparametric Instrumental Regressions: A Guide to Implementation

  • Samuele Centorrino EMAIL logo , Frederique Feve and Jean-Pierre Florens


We present a review on the implementation of regularization methods for the estimation of additive nonparametric regression models with instrumental variables. We consider various versions of Tikhonov, Landweber-Fridman and Sieve (Petrov-Galerkin) regularization. We review data-driven techniques for the sequential choice of the smoothing and the regularization parameters. Through Monte Carlo simulations, we discuss the finite sample properties of each regularization method for different smoothness properties of the regression function. Finally, we present an application to the estimation of the Engel curve for food in a sample of rural households in Pakistan, where a partially linear specification is described that allows one to embed other exogenous covariates.

JEL Classification: C01; C14; C18; C26

Corresponding author: Samuele Centorrino, Department of Economics, Stony Brook University, SBS Building Room N613, Stony Brook, New York, 11794, NY, USA, Phone: +6316327515, E-mail:


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Supplemental Material

The online version of this article (DOI: 10.1515/jem-2015-0010) offers supplementary material, available to authorized users.

Published Online: 2015-11-4
Published in Print: 2017-1-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

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