Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models

• 成果类型: Article
• 署名作者: Cox, Gregory; Shi, Xiaoxia
• 署名单位: National University of Singapore; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdac015
• 发表日期: 2023
• 页码: 201-228
• 关键词: confidence sets; parameters; validity; regions

摘要

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.