Sparse linear models and l1-regularized 2SLS with high-dimensional endogenous regressors and instruments

Ying Zhu

Journal of Econometrics, 2018, vol. 202, issue 2, 196-213

Abstract: We explore the validity of the 2-stage least squares estimator with l1-regularization in both stages, for linear triangular models where the numbers of endogenous regressors in the main equation and instruments in the first-stage equations can exceed the sample size, and the regression coefficients are sufficiently sparse. For this l1-regularized 2-stage least squares estimator, we first establish finite-sample performance bounds and then provide a simple practical method (with asymptotic guarantees) for choosing the regularization parameter. We also sketch an inference strategy built upon this practical method.

Keywords: High-dimensional statistics; Lasso; Sparse linear models; Endogeneity; Two-stage least squares (search for similar items in EconPapers)
JEL-codes: C14 C31 C36 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:202:y:2018:i:2:p:196-213

DOI: 10.1016/j.jeconom.2017.10.002

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