Additive Nonparametric Instrumental Regressions: A Guide to Implementation

Samuele Centorrino, Feve Frederique and Florens Jean-Pierre
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Feve Frederique: Toulouse School of Economics, University Toulouse I Capitole, Toulouse, France
Florens Jean-Pierre: Toulouse School of Economics, University Toulouse I Capitole, Toulouse, France

Journal of Econometric Methods, 2017, vol. 6, issue 1, 25

Abstract: 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.

Keywords: endogeneity; ill-posed inverse problem; instrumental variables; nonparametric (search for similar items in EconPapers)
JEL-codes: C01 C14 C18 C26 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (21)

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Working Paper: Additive Nonparametric Instrumental Regressions: A Guide to Implementation (2017)
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DOI: 10.1515/jem-2015-0010

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