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sidenav header background[5月14日] 计量金融与大数据分析workshop
发布日期：2021-05-11 11:38 来源：
题目：非参样本分割下的阈值回归（Threshold Regression with Nonparametric Sample Splitting）
时间：2021年5月14日（周五）10:00 AM -- 11:30 AM
地点：（线上形式）：点击链接入会，或添加至会议列表：https://meeting.tencent.com/s/QXkYOxdl6hXq
会议 ID：624 697 960
手机一键拨号入会
+8675536550000,,624697960# (中国大陆)
+85230018898,,,2,624697960# (中国香港)
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+8675536550000 (中国大陆)
+85230018898 (中国香港)
（线下形式）：经济学院107会议室
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮、胡博
（经济学院）王一鸣、王熙、刘蕴霆
主讲人：王宇龙（雪城大学助理教授）
报告摘要：
This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold. We allow the observations to be cross-sectionally dependent so that the model can be applied to determine an unknown spatial border for sample splitting over a random field. We derive the uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator. We also obtain the root-n consistency and the asymptotic normality of the regression coefficient estimator. Our model has broad empirical relevance as illustrated
by estimating the tipping point in social segregation problems as a function of demographic characteristics; and determining metropolitan area boundaries using nighttime light intensity collected from satellite imagery. We find that the new empirical results are substantially different from those in the existing studies.
主讲人简介：
Yulong Wang is an Assistant Professor of Economics in the Maxwell School and a Senior Research Associate in the Center for Policy Research. Before joining Syracuse University, Wang earned a B.A. from Tsinghua University and Ph.D. in economics from Princeton University. His current research focuses on designing new econometric tools in the non-standard instances when the classic asymptotically Gaussian framework fails to provide good performance. These tools are strongly motivated by empirical applications. Leading examples include estimating the location of the tipping point in social segregation, determining metropolitan areas based on nighttime light intensity, inference about winner’s properties in auctions, and studying the cost of extreme events such as natural disasters.
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[5月14日] 计量金融与大数据分析workshop
发布日期：2021-05-11 11:38 来源：
题目：非参样本分割下的阈值回归（Threshold Regression with Nonparametric Sample Splitting）
时间：2021年5月14日（周五）10:00 AM -- 11:30 AM
地点：（线上形式）：点击链接入会，或添加至会议列表：https://meeting.tencent.com/s/QXkYOxdl6hXq
会议 ID：624 697 960
手机一键拨号入会
+8675536550000,,624697960# (中国大陆)
+85230018898,,,2,624697960# (中国香港)
根据您的位置拨号
+8675536550000 (中国大陆)
+85230018898 (中国香港)
（线下形式）：经济学院107会议室
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮、胡博
（经济学院）王一鸣、王熙、刘蕴霆
主讲人：王宇龙（雪城大学助理教授）
报告摘要：
This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold. We allow the observations to be cross-sectionally dependent so that the model can be applied to determine an unknown spatial border for sample splitting over a random field. We derive the uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator. We also obtain the root-n consistency and the asymptotic normality of the regression coefficient estimator. Our model has broad empirical relevance as illustrated
by estimating the tipping point in social segregation problems as a function of demographic characteristics; and determining metropolitan area boundaries using nighttime light intensity collected from satellite imagery. We find that the new empirical results are substantially different from those in the existing studies.
主讲人简介：
Yulong Wang is an Assistant Professor of Economics in the Maxwell School and a Senior Research Associate in the Center for Policy Research. Before joining Syracuse University, Wang earned a B.A. from Tsinghua University and Ph.D. in economics from Princeton University. His current research focuses on designing new econometric tools in the non-standard instances when the classic asymptotically Gaussian framework fails to provide good performance. These tools are strongly motivated by empirical applications. Leading examples include estimating the location of the tipping point in social segregation, determining metropolitan areas based on nighttime light intensity, inference about winner’s properties in auctions, and studying the cost of extreme events such as natural disasters.
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