Revisiting the Many Instruments Problem using Random Matrix Theory

Helmut Farbmacher, Rebecca Groh, Michael M\"uhlegger and Gabriel Vollert

Papers from arXiv.org

Abstract: We use recent results from the theory of random matrices to improve instrumental variables estimation with many instruments. In settings where the first-stage parameters are dense, we show that Ridge lowers the implicit price of a bias adjustment. This comes along with improved (finite-sample) properties in the second stage regression. Our theoretical results nest existing results on bias approximation and bias adjustment. Moreover, it extends them to settings with more instruments than observations.

Date: 2024-08
New Economics Papers: this item is included in nep-ecm
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