Agglomerative hierarchical clustering for selecting valid instrumental variables

Bibliographic Data

ID21653350
AuthorsNicolas Apfel (0000-0001-5771-6745, Department of Economics University of Southampton UK, corresponding author), Xiaoran Liang (0000-0002-2142-2427, Exeter Medical School University of Exeter UK)
Year2024
Volume39
Issue7
Pages1201-1219
Publication date2024-11-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.3078
OpenAlexW4400363509
LanguageEN
References cited23

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

Cluster analysis · Econometrics · Hierarchical clustering · Instrumental variable · Statistics · Computer Science · Energy Load and Power Forecasting · Mathematics · Monetary Policy and Economic Impact · Statistical Methods and Inference · Artificial Intelligence

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