Inference in partially identified models with many moment inequalities using Lasso

TL;DR

This paper introduces a new two-step inference method for partially identified models with many moment inequalities, combining Lasso-based selection with existing techniques to improve power and maintain size control.

Contribution

It proposes a novel Lasso-based first step for moment inequality selection, enhancing inference in high-dimensional partially identified models.

Findings

Controls asymptotic size uniformly across parameters and data distributions.

Improves power compared to existing two-step methods in simulations.

Implementation is straightforward via thresholding standardized sample averages.

Abstract

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 (2018a) (CCK18, hereafter) with a first step moment inequality selection based on the Lasso. Our method controls asymptotic 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 CCK18 for large parts of the parameter space, both in theory and in simulations. Finally, we show that our Lasso-based first step can be implemented by thresholding standardized sample averages, and so it is straightforward to implement.