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Nicolas Apfel
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agglomerative hierarchical clustering for selecting valid instrumental variables (replication data)

Description: This reproduction package includes: 1. A text-file describing how to access the data used in the application. 2. R-Code necessary for the replication of results.

Abstract: We propose a procedure which 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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Apfel, Nicolas; Liang, Xiaoran (2024): Agglomerative hierarchical clustering for selecting valid instrumental variables (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2024142.1250902445

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