【主讲】Takuya Ura (University of California, Davis)
【主题】Instrumental Variable Quantile Regression with Misclassification
【时间】2017年7月4日 (周二) 15：30-17：00
【地点】上海财经大学经济学院楼702室
【语言】英文
【摘要】This paper studies the instrumental variable quantile regression model (Chernozhukov and hansen, 2005) when a binary treatment variable is possibly misclassified and endogenous. It has two identification results. First, I show that, under the stochastic monotonicity condition (Small and Tan, 2007; and DiNardo and Lee, 2011), the reduced-form quantile treatment effect is biased towards zero compared to the structural quantile treatment effect and therefore can be used as a lower bound for it. The reduced-form quantile treatment effect is the quantile treatment effect of the instrumental variable on the outcome variable, and is available even without any measurement for the treatment variable. Second, I derive moment conditions for the structural quantile function under standard assumptions about the measurement error. The moment conditions can be used for an inference via existing methods for moment inequalities.
