Cross-Validation Selection of Regularisation Parameter(s) for Semiparametric Transformation Models

We propose cross-validation criteria for the selection of regularisation parameter(s) in the semiparametric instrumental variable transformation model proposed in Florens, J.-P., and S. Sokullu [2016]. In the presence of an endogenous regressor, this model is characterized by the need to choose two...

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Bibliographic Details
Published in:Annals of economics and statistics 2017-12 (128), p.67-108
Main Authors: Senay Sokullu, Sami Stouli
Format: Article
Language:English
Subjects:
Economic models
Instrumental variables estimation
Inverse problems
Mathematical independent variables
Mathematical transformations
Mathematical vectors
Parametric models
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Summary:We propose cross-validation criteria for the selection of regularisation parameter(s) in the semiparametric instrumental variable transformation model proposed in Florens, J.-P., and S. Sokullu [2016]. In the presence of an endogenous regressor, this model is characterized by the need to choose two regularisation parameters, one for the structural function and one for the transformation of the outcome. We consider two-step and simultaneous criteria, and analyze the finite-sample performance of the estimator using the corresponding regularisation parameters by means of several Monte-Carlo simulations. Our numerical experiments show that simultaneous selection of regularisation parameters provides significant improvements in the performance of the estimator. We also apply our methods to the choice of regularisation parameters in the estimation of two-sided network effects in the German magazine industry. JEL: C14, C26, L14 / KEY WORDS: Nonparametric IV Regression, Transformation Models, Cross-Validation, Tikhonov Regularisation, Ill-Posed Inverse Problems.
ISSN:2115-4430
1968-3863
DOI:10.15609/annaeconstat2009.128.0067