Subvector Inference in Partially Identified Models with Many Moment Inequalities

Understand

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.