Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models
提出一种矩不等式检验方法，在正态模型下精确控制尺寸，渐近正态下均匀渐近精确，无需模拟和调参，适用于参数置信集构建及含线性干扰参数的条件矩不等式子向量推断。
Abstract We propose a simple test for moment inequalities that has exact size in normal models with known variance and has uniformly asymptotically exact size under asymptotic normality. The test compares the quasi-likelihood ratio statistic to a chi-squared critical value, where the degree of freedom is the rank of the inequalities that are active in finite samples. The test requires no simulation and thus is computationally fast and especially suitable for constructing confidence sets for parameters by test inversion. It uses no tuning parameter for moment selection and yet still adapts to the slackness of the moment inequalities. Furthermore, we show how the test can be easily adapted to inference on subvectors in the common empirical setting of conditional moment inequalities with nuisance parameters entering linearly. User-friendly Matlab code to implement the test is provided.