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Subvector Inference in Partially Identified Models with Many Moment Inequalities by Belloni, Alexandre; Bugni, Federico; Chernozhukov, Victor is a scholarly article available to read on EtoBox.

What is Subvector Inference in Partially Identified Models with Many Moment Inequalities about?

This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities. This framework allows the number of moment conditions to grow with the sample size, possibly at exponential rates. Our main motivating application is subvector inference, i.e., inference on a single component of the partially identified parameter vector associated with a treatment effect or a policy variable of interest. Our inference method compares a MinMax test statistic (minimum over parameters satisfying $H_0$ and maximum over moment inequalities) against critical values that are based on bootstrap approximations or analytical bounds. We show that this method controls asymptotic size uniformly over a large class of data generating processes despite the partially identified many moment inequality setting. The finite sample analysis allows us to obtain explicit rates of convergence on the size control. Our results are based on combining non-asymptotic approximations and new high-dimensional central limit theorems for the MinMax of the components of random matrices. Unlike the previous literature on functional inference in partially identified models,

Author

Belloni, Alexandre; Bugni, Federico; Chernozhukov, Victor

Published

2018

Language

EN