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Title: Agglomerative hierarchical clustering for selecting valid instrumental variables Authors:  Xiaoran Liang - University of Bristol (United Kingdom) [presenting]
Abstract: An instrumental variable (IV) selection procedure is proposed that combines the agglomerative hierarchical clustering method and the Hansen-Sargan overidentification test for selecting valid instruments for IV estimation from a large set of candidate instruments. Some of the instruments may be invalid in the sense that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method can achieve oracle selection and estimation results. Compared to the previous IV selection methods, our method has the advantage that it can deal with the weak instruments problem effectively, and can be easily extended to settings where there are multiple endogenous regressors and heterogeneous treatment effects. We conduct Monte Carlo simulations to examine the performance of our method. The simulation results show that our method achieves oracle selection and estimation results in both single and multiple endogenous regressors settings in large samples when instruments are strong. The method works well, even when many instruments are weak, with single or multiple regressors. We apply our method to the estimation of the effects of immigration on wages in the US.