Inference in partially identified models with many moment inequalities using Lasso

This paper considers inference in a partially identified moment (in)equality model with many moment inequalities. We propose a novel two-step inference procedure that combines the methods proposed by Chernozhukov, Chetverikov and Kato (2014c) (CCK14, hereafter) with a first-step moment inequality selection based on the Lasso. Our method controls size uniformly, both in underlying parameter and data distribution. Also, the power of our method compares favorably with that of the corresponding two-step method in CCK14 for large parts of the parameter space, both in theory and in simulations. Finally, our Lasso-based first step is straightforward to implement.