各位學界的朋友，大家好：

國立臺灣大學計量理論與應用研究中心 (CRETA)、國立臺灣大學財務金融學系及臺灣經濟計量學會 (TES) 將於 2018 年 12 月 21 日共同舉辦 CRETA Seminar。

為方便場地安排及人數預估，欲參加CRETA Seminar的朋友們，煩請事先報名。

為方便臺灣經濟計量學會 (TES) 會員繳納 108 年度會費，本次活動開放現場繳納會費，亦歡迎大家介紹非會員朋友加入 TES。更多研討會資訊請見 TES 網站：https://ppt.cc/fV9Czx
此外，臺灣經濟計量學會新開設臉書粉絲專頁，歡迎各位舊雨新知按讚及追蹤我們各項活動的最新訊息！網址如下：https://ppt.cc/f1wxJx

當天講義將優先提供給報名者，歡迎各位踴躍參加！！

【日期】2018 年 12 月 21 日 (週五) 下午2:00~5:00

【地點】國立臺灣大學管理學院二號館三樓 304 教室

【講者】楊睿中教授(國立清華大學經濟學系)

【演講主題】Tree, Random Forest, Gradient Boosting, Double Machine Learning, and a Discussion on Propensity Score Matching
 
【講題摘要】
The gradient boosting and the random forest have been widely used in empirical applications. Computationally, the gradient boosting performs the functional gradient descent on the loss function by repeatedly fitting a weak learner, typically a shallow classification and regression tree (CART), to the residuals. However, since CART is a special case of multivariate adaptive regression splines (MARS), gradient boosting with shrinkage can be viewed as an infinitesimal forward-stagewise spline regression, which is a special version of a (constant) spline regression with a L1 penalty, a.k.a., the least absolute shrinkage and selection operator (LASSO). On the other hand, the random forest is a combination of the bootstrap aggregating (bagging) and the CART. Nevertheless, it has been shown that the random forest can also be viewed as an adaptively weighted k potential nearest neighbors (k-PNN) method.

Recently Chernozhukov et al. (2018) proposed a new double/debiased machine learning frame-work  (DML)  for  the  estimation  and  inference  of  low-dimensional  parameters  in  the  presence  of high-dimensional nuisance parameters. In DML, the low-dimensional parameters of interest are estimated using the Neyman orthogonal moment and the cross-fitting technique, while the nuisance parameters are estimated by some machine learning algorithms with sufficient rates of convergence.  We show that a) regression trees and random forests in general do not have the sufficient rates and may result in serious bias and size distortion, and b) when unknown nuisance functions are additive, the gradient boosting with stumps provide consistent estimation and correct inference for the treatment effect.

We apply our methods to recent debate of the treatment effect of the Big N auditors to the audit quality. Consistent to the result of DeFond et al. (2016), who use 3,000 designs of the propensity score matching, our method supports the existing of the Big N effect during 1988 to 2006 in U.S. DML-GB also identifies the non-linear associations of the firm characteristics and the audit quality.
 
【講者介紹】
楊睿中教授 (國立清華大學經濟學系助理教授，個人網頁：https://ppt.cc/fTH6dx。
 
【會程安排】
下午 1：30 ~ 2：00報到

下午 2：00 ~ 3：20 First session

下午 3：20 ~ 3：40 Tea Break 

下午 3：40 ~ 5：00 Second session

【報名網址】https://ppt.cc/fXUk4x 

【報名費用】台灣大學在學學生及現任教職員和台灣經濟計量學會會員為免費參加，其他參加者報名費為NT$200 (當天將開放現場繳交台灣經濟計量學會 2019 年年度會費)。

【報名期限】2018/12/20 (四) 13:30