Ill-posed Estimation in High-Dimensional Models with Instrumental Variables

TL;DR

This paper introduces a novel desparsified instrumental variable Lasso estimator for high-dimensional models with weak identification, enabling valid inference on low-dimensional parameters.

Contribution

It proposes a new estimator that corrects for bias in high-dimensional IV models with shrinking eigenvalues, allowing for asymptotic normality and inference.

Findings

Estimator converges at a rate depending on the eigenvalue structure.

Asymptotic normality of linear combinations of the estimator.

Simulation results demonstrate finite sample performance.

Abstract

This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector $\beta^0$ which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by $M$, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to $\beta^0$ at a rate depending on the mapping properties of $M$ captured by a sparse link condition. Linear combinations of our estimator of $\beta^0$ are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of $\beta^0$. In Monte-Carlo simulations we analyze the finite sample behavior of our estimator.