Agglomerative hierarchical clustering for selecting valid instrumental variables
Agglomerative hierarchical clustering for selecting valid instrumental variables
We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.
Apfel, Nicolas
53d7e18d-dc96-4772-abab-6f02aeafbbde
Liang, Xiaoran
76838db9-131f-4cce-a022-d20ad9806b99
Apfel, Nicolas
53d7e18d-dc96-4772-abab-6f02aeafbbde
Liang, Xiaoran
76838db9-131f-4cce-a022-d20ad9806b99
Apfel, Nicolas and Liang, Xiaoran
(2024)
Agglomerative hierarchical clustering for selecting valid instrumental variables.
Journal of Applied Econometrics.
(doi:10.1002/jae.3078).
Abstract
We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.
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2101.05774v3
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Accepted version, 2024, including Supplemental Material
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J of Applied Econometrics - 2024 - Apfel - Agglomerative hierarchical clustering for selecting valid instrumental variables
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Accepted/In Press date: 5 May 2024
e-pub ahead of print date: 5 July 2024
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© 2024 The Author(s). Journal of Applied Econometrics published by John Wiley & Sons Ltd.
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Local EPrints ID: 491311
URI: http://eprints.soton.ac.uk/id/eprint/491311
ISSN: 0883-7252
PURE UUID: 3fb99342-b79d-4c24-b807-94990467e893
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Date deposited: 19 Jun 2024 16:49
Last modified: 23 Jul 2024 16:48
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Author:
Nicolas Apfel
Author:
Xiaoran Liang
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