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sidenav header background[3月29日]计量、金融和大数据分析workshop
发布日期：2019-03-28 10:00 来源：
【讲座时间】：3月29日（周五）上午9:00-11:00
【讲座地点】：北京大学经济学院，107会议室
【讲座标题】：Estimating Endogenous Treatment Effect Using High-Dimensional Instruments with an Application to the Olympic Effect
【摘要】：Endogenous treatments are commonly encountered in program evaluations using observational data where the selection-on-observables assumption does not hold. In this paper, we develop a two-stage approach to estimate endogenous treatment effects using high-dimensional instrumental variables. In the first stage, instead of using a linear reduced form regression in the conventional two-stage least squares approach, we propose a new high-dimensional logistic reduced form model with the SCAD penalty to approximate the optimal instrument. In the second stage, we replace the original treatment variable by its estimated propensity score and run a least squares regression to obtain the penalized Logistic-regression Instrumental Variables Estimator (LIVE). We show that the proposed LIVE is root-n consistent to the true average treatment effect, asymptotically normal and achieves the semiparametric efficiency bound. Monte Carlo simulations demonstrate that the LIVE outperforms the traditional TSLS estimator and the post-Lasso estimator for the endogenous treatment effects. Moreover, in the empirical study, we investigate whether the Olympic Games could facilitate the host nation's economic growth using data from 163 countries. The proposed LIVE estimator shows a strong Olympic effect on the host nation's economic growth.
This is a joint work with Wei Zhong, Wei Zhou and Yang Gao.
【主讲人】：范青亮（副教授）
【主讲人简介】：厦门大学王亚南经济研究院、经济学院统计系副教授，博士生导师，入选福建省“高校杰出青年科研人才培育计划”。主要研究成果发表于Journal of Econometrics, Journal of Business & Economic Statistics等国际顶级刊物和IEEE GlobalSIP等重要国际会议。
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首页
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[3月29日]计量、金融和大数据分析workshop
发布日期：2019-03-28 10:00 来源：
【讲座时间】：3月29日（周五）上午9:00-11:00
【讲座地点】：北京大学经济学院，107会议室
【讲座标题】：Estimating Endogenous Treatment Effect Using High-Dimensional Instruments with an Application to the Olympic Effect
【摘要】：Endogenous treatments are commonly encountered in program evaluations using observational data where the selection-on-observables assumption does not hold. In this paper, we develop a two-stage approach to estimate endogenous treatment effects using high-dimensional instrumental variables. In the first stage, instead of using a linear reduced form regression in the conventional two-stage least squares approach, we propose a new high-dimensional logistic reduced form model with the SCAD penalty to approximate the optimal instrument. In the second stage, we replace the original treatment variable by its estimated propensity score and run a least squares regression to obtain the penalized Logistic-regression Instrumental Variables Estimator (LIVE). We show that the proposed LIVE is root-n consistent to the true average treatment effect, asymptotically normal and achieves the semiparametric efficiency bound. Monte Carlo simulations demonstrate that the LIVE outperforms the traditional TSLS estimator and the post-Lasso estimator for the endogenous treatment effects. Moreover, in the empirical study, we investigate whether the Olympic Games could facilitate the host nation's economic growth using data from 163 countries. The proposed LIVE estimator shows a strong Olympic effect on the host nation's economic growth.
This is a joint work with Wei Zhong, Wei Zhou and Yang Gao.
【主讲人】：范青亮（副教授）
【主讲人简介】：厦门大学王亚南经济研究院、经济学院统计系副教授，博士生导师，入选福建省“高校杰出青年科研人才培育计划”。主要研究成果发表于Journal of Econometrics, Journal of Business & Economic Statistics等国际顶级刊物和IEEE GlobalSIP等重要国际会议。
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