Zhu, Ying (2013): Sparse Linear Models and TwoStage Estimation in HighDimensional Settings with Possibly Many Endogenous Regressors.
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Abstract
This paper explores the validity of the twostage estimation procedure for sparse linear models in highdimensional settings with possibly many endogenous regressors. In particular, the number of endogenous regressors in the main equation and the instruments in the firststage equations can grow with and exceed the sample size n. The analysis concerns the exact sparsity case, i.e., the maximum number of nonzero components in the vectors of parameters in the firststage equations, k1, and the number of nonzero components in the vector of parameters in the secondstage equation, k2, are allowed to grow with n but slowly compared to n. I consider the highdimensional version of the twostage least square estimator where one obtains the fitted regressors from the firststage regression by a least square estimator with l_1regularization (the Lasso or Dantzig selector) when the firststage regression concerns a large number of instruments relative to n, and then construct a similar estimator using these fitted regressors in the secondstage regression. The main theoretical results of this paper are nonasymptotic bounds from which I establish sufficient scaling conditions on the sample size for estimation consistency in l_2norm and variableselection consistency (i.e., the twostage highdimensional estimators correctly select the nonzero coefficients in the main equation with high probability). A technical issue regarding the socalled "restricted eigenvalue (RE) condition" for estimation consistency and the "mutual incoherence (MI) condition" for selection consistency arises in the twostage estimation from allowing the number of regressors in the main equation to exceed n and this paper provides analysis to verify these RE and MI conditions. Depending on the underlying assumptions that are imposed, the upper bounds on the l_2error and the sample size required to obtain these consistency results differ by factors involving k1 and/or k2. Simulations are conducted to gain insight on the finite sample performance of the highdimensional twostage estimator.
Item Type:  MPRA Paper 

Original Title:  Sparse Linear Models and TwoStage Estimation in HighDimensional Settings with Possibly Many Endogenous Regressors 
Language:  English 
Keywords:  Highdimensional statistics; Lasso; sparse linear models; endogeneity; twostage estimation 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C31  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C36  Instrumental Variables (IV) Estimation 
Item ID:  49846 
Depositing User:  Ms Ying Zhu 
Date Deposited:  18 Sep 2013 12:45 
Last Modified:  09 Oct 2019 04:52 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/49846 
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