Identification and inference of possibly bi-directional causal relationships with invalid instrumental variables
Holder： Xiaoxia Shi（University of Wisconsin-Madison）
Time：2025-04-17 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
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 GCC test is applicable to a broad set of inequality testing problems, including subvector inference and specification testing for linear unconditional moment (in)equality models and inference for parameters bounded by linear programs. The new test uses a two-step GMM type test statistic and a chi-squared critical value with a data-dependent freedom that can be calculated by an elementary formula. Its simple structure and tuning-parameter-free implementation makes it attractive for practical use. We establish uniform asymptotic validity of the test and demonstrate its good power in Monte Carlo simulations.
About the Speaker:
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.
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