Liu, ChuAn and Tao, Jing (2016): Model selection and model averaging in nonparametric instrumental variables models.

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Abstract
This paper considers the problem of choosing the regularization parameter and the smoothing parameter in nonparametric instrumental variables estimation. We propose a simple Mallows’ Cptype criterion to select these two parameters simultaneously. We show that the proposed selection criterion is optimal in the sense that the selected estimate asymptotically achieves the lowest possible mean squared error among all candidates. To account for model uncertainty, we introduce a new model averaging estimator for nonparametric instrumental variables regressions. We propose a Mallows criterion for the weight selection and demonstrate its asymptotic optimality. Monte Carlo simulations show that both selection and averaging methods generally achieve lower root mean squared error than other existing methods. The proposed methods are applied to two empirical examples, the effect of class size question and Engel curve.
Item Type:  MPRA Paper 

Original Title:  Model selection and model averaging in nonparametric instrumental variables models 
Language:  English 
Keywords:  Illposed inverse problem, Mallows criterion, Model averaging, Model selection, Nonparametric instrumental variables, Series estimation 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C26  Instrumental Variables (IV) Estimation C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection 
Item ID:  69492 
Depositing User:  Dr CHUAN LIU 
Date Deposited:  01 Mar 2016 14:20 
Last Modified:  29 Sep 2019 10:04 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/69492 