On the instrumental variable estimation with many weak and invalid instruments

• URL: http://arxiv.org/abs/2207.03035v2
• Date: Tue, 5 Dec 2023 15:06:56 GMT
• Title: On the instrumental variable estimation with many weak and invalid instruments
• Authors: Yiqi Lin, Frank Windmeijer, Xinyuan Song, Qingliang Fan
• Abstract summary: We discuss the fundamental issue of computation in instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest properties", which is is equivalent to a sparse penalty structure, we investigate and prove the advantages of a surrogate-step identification method. We propose a surrogate-step selection estimation method that aligns with the sparse identification condition.
• Score: 1.837552179215311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest rule", which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surrogate sparsest penalty that aligns with the identification condition and provides oracle sparse structure simultaneously. Desirable theoretical properties are derived for the proposed estimator with weaker IV strength conditions compared to the previous literature. Finite sample properties are demonstrated using simulations and the selection and estimation method is applied to an empirical study concerning the effect of BMI on diastolic blood pressure.

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