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Testing Inequalities Linear in Nuisance Parameters
（干扰参数中不等式的线性检验）
主讲人： Xiaoxia Shi（University of Wisconsin Madison）
主持老师：（北大经院）王法
参与老师：（太阳集团）王一鸣、王熙、刘蕴霆
时间：2025年3月14日（周五） 10:00-11:30
地点（线下）：太阳集团tcy8722107会议室
报告摘要：
This paper proposes a new test for inequalities that are linear in possibly partially identified nuisance parameters, called the generalized conditional chi-squared (GCC) test. The test offers a simple, tuning parameter free solution to a broad set of problems including subvector inference for linear unconditional moment (in)equality models and nonparametric instrumental variable models with shape restrictions, and inference for parameters bounded by linear programs. A challenge in these settings is to properly account for the estimation error of the uknown Jacobian matrix. Our GCC test addresses the challenge using a two-step GMM-like test statistic, ensuring uniform asymptotic validity under a stable rank condition, one that we link to the IV strength condition behind standard extremum estimators. Meanwhile, we also derive an analytical formula for the critical value that makes the computation of the test elementary.
主讲人简介：
Xiaoxia Shi is the Lowell and Leila Robinson Chair Professor in the Economics Department of the University of Wisconsin Madison. She is a CoEditor of Econometric Theory, an Associate Editor of Quantitative Economics and the former Editor of Review of Economics and Statistics. Her research has focused on testing inequality hypotheses, inference for parameters defined by moment inequality models, semiparameteic identification of discrete choice models, and model selection tests.
