Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments

Eric Gautier; Christiern Rose

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arXiv · 2211.02249

Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments

Eric Gautier·Christiern Rose

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Abstract

We propose and implement an approach to inference in linear instrumental variables models which is simultaneously robust and computationally tractable. Inference is based on self-normalization of sample moment conditions, and allows for (but does not require) many (relative to the sample size), weak, potentially invalid or potentially endogenous instruments, as well as for many regressors and conditional heteroskedasticity. Our coverage results are uniform and can deliver a small sample guarantee. We develop a new computational approach based on semidefinite programming, which we show can equally be applied to rapidly invert existing tests (e.g,. AR, LM, CLR, etc.).

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BibTeX RIS

Eric Gautier, Christiern Rose. 2022-11-04. Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments. https://arxiv.org/abs/2211.02249

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