# Simple subvector inference on sharp identified set in affine models

## TL;DR

This paper introduces a regularized support function estimator for sharp bounds in affine models with polygon-shaped identified sets, enabling efficient, uniformly valid inference even in high-dimensional settings.

## Contribution

It proposes a novel regularized estimator with desirable asymptotic properties and computational efficiency for constructing confidence sets in partially identified affine models.

## Key findings

- Estimator has a Gaussian limit despite flat faces.
- Bias from regularization does not affect first-order distribution.
- Method is computationally feasible for high-dimensional problems.

## Abstract

This paper studies a regularized support function estimator for bounds on components of the parameter vector in the case in which the identified set is a polygon. The proposed regularized estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice versa); (ii) the bias from regularization does not enter the first-order limiting distribution; (iii) the estimator remains consistent for sharp (non-enlarged) identified set for the individual components even in the non-regualar case. These properties are used to construct \emph{uniformly valid} confidence sets for an element $\theta_{1}$ of a parameter vector $\theta\in\mathbb{R}^{d}$ that is partially identified by affine moment equality and inequality conditions. The proposed confidence sets can be computed as a solution to a small number of linear and convex quadratic programs, leading to a substantial decrease in computation time and guarantees a global optimum. As a result, the method provides a uniformly valid inference in applications in which the dimension of the parameter space, $d$, and the number of inequalities, $k$, were previously computationally unfeasible ($d,k=100$). The proposed approach can be extended to construct confidence sets for intersection bounds, to construct joint polygon-shaped confidence sets for multiple components of $\theta$, and to find the set of solutions to a linear program. Inference for coefficients in the linear IV regression model with an interval outcome is used as an illustrative example.