GMM estimation in partial linear models with endogenous covariates causing an over-identified problem

Baicheng Chen, Hua Liang and Yong Zhou

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 11, 3168-3184

Abstract: We study partial linear models where the linear covariates are endogenous and cause an over-identified problem. We propose combining the profile principle with local linear approximation and the generalized moment methods (GMM) to estimate the parameters of interest. We show that the profiled GMM estimators are root− n consistent and asymptotically normally distributed. By appropriately choosing the weight matrix, the estimators can attain the efficiency bound. We further consider variable selection by using the moment restrictions imposed on endogenous variables when the dimension of the covariates may be diverging with the sample size, and propose a penalized GMM procedure, which is shown to have the sparsity property. We establish asymptotic normality of the resulting estimators of the nonzero parameters. Simulation studies have been presented to assess the finite-sample performance of the proposed procedure.

Date: 2016
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DOI: 10.1080/03610926.2014.901363

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