Fan, Jianqing and Liao, Yuan (2012): Endogeneity in ultrahigh dimension.

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Abstract
Most papers on highdimensional statistics are based on the assumption that none of the regressors are correlated with the regression error, namely, they are exogenous. Yet, endogeneity arises easily in highdimensional regression due to a large pool of regressors and this causes the inconsistency of the penalized leastsquares methods and possible false scientic discoveries. A necessary condition for model selection of a very general class of penalized regression methods is given, which allows us to prove formally the inconsistency claim. To cope with the possible endogeneity, we construct a novel penalized focussed generalized method of moments (FGMM) criterion function and oer a new optimization algorithm. The FGMM is not a smooth function. To establish its asymptotic properties, we rst study the model selection consistency and an oracle property for a general class of penalized regression methods. These results are then used to show that the FGMM possesses an oracle property even in the presence of endogenous predictors, and that the solution is also near global minimum under the overidentication assumption. Finally, we also show how the semiparametric efficiency of estimation can be achieved via a twostep approach.
Item Type:  MPRA Paper 

Original Title:  Endogeneity in ultrahigh dimension 
Language:  English 
Keywords:  Focused GMM, Sparsity recovery, Endogenous variables, Oracle property, Conditional moment restriction, Estimating equation, Over identi cation, Global minimization, Semiparametric efficiency 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  38698 
Depositing User:  Yuan Liao 
Date Deposited:  10 May 2012 01:43 
Last Modified:  26 Sep 2019 21:46 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/38698 