报告题目：Regression Kink Design: Theory and Practice1

时间：2016年6月13日（星期一）14：00-15：30

地点：学院南路校区，学术会堂712

报告人：Zhuan Pei，现任美国康奈尔大学（Cornell University）政策分析与管理系助理教授，2012年获得美国普林斯顿大学经济学博士学位，研究领域包括劳动经济学、应用微观计量经济学、公共政策分析。已在国际权威学术期刊如Econometrica, American Economic Review: Papers & Proceedings等发表高水平论文数篇。此外还担任American Economic Journal: Applied Economics, Economic Inquiry, The Economic Journal, Journal of Econometrics, Journal of Human Resources, The Review of Economics and Statistics等多个国际一流学术期刊的审稿人。

报告摘要：

A regression kink design (RKD) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. We show that although recent developments in nonparametric estimation (e.g. Imbens and Kalyanaraman (2012) and Calonico et al. (2014b)) may be interpreted by practitioners as pointing to a “default” estimation procedure, there is no single “optimal” approach. In particular, Monte Carlo simulations based on data generating processes that closely resemble the data show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

[编辑]：孙颖