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计量金融与大数据分析Workshop:Inference on Union Bounds with Applications to DiD, RDD, Bunching, and Structural Counterfactuals
发布日期：2024-03-22 12:00 来源：
题目：Inference on Union Bounds with Applications to DiD, RDD, Bunching, and Structural Counterfactuals
摘要：A union bound is a union of multiple bounds. Union bounds occur in a wide variety of empirical settings, from relaxations of the difference-in-differences parallel trends assumption to counterfactual analysis with partially identified structural parameters. In this paper, I provide the first general and systematic study of inference on these kinds of bounds. When the union is taken over a finite set, I propose a confidence interval based on modified conditional inference. I show that it improves upon existing methods in a large set of data generating processes. When the union is taken over an infinite set, I consider the set defined by moment inequalities, as is common in practice. I then propose a calibrated projection based inference procedure that generalizes results from the moment inequality subvector inference literature and is computationally simple. Finally, the new procedures give statistically significant results while the pre-existing alternatives do not in two empirical applications, the sensitivity analysis in Dustmann, Lindner, Schönberg, Umkehrer, and Vom Berge (2022) and the counterfactual analysis in Dickstein and Morales (2018).
主讲人：Xinyue Bei, Duke University
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年3月22日（周五）10:00 AM -- 11:30 AM
地点：经济学院107
主讲人简介：
Dr Xinyue Bei is an econometrician working primarily on inference in partially identified models with applications in structural models, sensitivity analysis, and counterfactual analysis. She is also interested in hypothesis tests in nonstandard situations, with applications to normal mixtures and regime switching, sample selectivity, skew normal distributions, and serial correlation.
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计量金融与大数据分析Workshop:Inference on Union Bounds with Applications to DiD, RDD, Bunching, and Structural Counterfactuals
发布日期：2024-03-22 12:00 来源：
题目：Inference on Union Bounds with Applications to DiD, RDD, Bunching, and Structural Counterfactuals
摘要：A union bound is a union of multiple bounds. Union bounds occur in a wide variety of empirical settings, from relaxations of the difference-in-differences parallel trends assumption to counterfactual analysis with partially identified structural parameters. In this paper, I provide the first general and systematic study of inference on these kinds of bounds. When the union is taken over a finite set, I propose a confidence interval based on modified conditional inference. I show that it improves upon existing methods in a large set of data generating processes. When the union is taken over an infinite set, I consider the set defined by moment inequalities, as is common in practice. I then propose a calibrated projection based inference procedure that generalizes results from the moment inequality subvector inference literature and is computationally simple. Finally, the new procedures give statistically significant results while the pre-existing alternatives do not in two empirical applications, the sensitivity analysis in Dustmann, Lindner, Schönberg, Umkehrer, and Vom Berge (2022) and the counterfactual analysis in Dickstein and Morales (2018).
主讲人：Xinyue Bei, Duke University
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年3月22日（周五）10:00 AM -- 11:30 AM
地点：经济学院107
主讲人简介：
Dr Xinyue Bei is an econometrician working primarily on inference in partially identified models with applications in structural models, sensitivity analysis, and counterfactual analysis. She is also interested in hypothesis tests in nonstandard situations, with applications to normal mixtures and regime switching, sample selectivity, skew normal distributions, and serial correlation.
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