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厦门大学 钟威教授: Estimating Endogenous Treatment Effect Using High-Dimensional Instruments

光华讲坛——社会名流与企业家论坛第5307期

主题:Estimating Endogenous Treatment Effect Using High-Dimensional Instruments

主讲人:厦门大学钟威教授

主持人:统计学院 林华珍教授

时间:2019年4月26日(星期四)上午11:00-12:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

主讲人简介:

钟威:美国宾夕法尼亚州立大学统计学博士,厦门大学王亚南经济研究院、经济学院教授、博导,经济学院院长助理。

主要内容:

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 (TSLS) 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. We look forward to hearing you. Your kind consideration is highly appreciated.