Additive Nonparametric Instrumental Regressions: A Guide to Implementation

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...

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Bibliographic Details
Published in:Journal of econometric methods 2017-01, Vol.6 (1), p.1795
Main Authors: Centorrino, Samuele, Feve, Frederique, Florens, Jean-Pierre
Format: Article
Language:English
Subjects:
C01
C14
C18
C26
Economic models
endogeneity
ill-posed inverse problem
instrumental variables
Monte Carlo simulation
nonparametric
Nonparametric statistics
Regression analysis
Regularization methods
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Summary: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.
ISSN:2194-6345
2156-6674
DOI:10.1515/jem-2015-0010