Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7358135 | Journal of Econometrics | 2018 | 38 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Ying Zhu,