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2018 年 12 月 21 日 CRETA Seminar


2018 年 12 月 21 日 CRETA Seminar


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 日舉辦 CRETA Seminar。相關資訊如下。


 12  21  CRETA Seminar

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


演講主題:Tree, Random Forest, Gradient Boosting, Double Machine Learning, and a Discussion on Propensity Score Matching



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.

About speaker:


楊睿中教授 (國立清華大學經濟學系助理教授,個人網頁:https://sites.google.com/site/juichungyang/)。


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。




                 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


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:http://www.tesociety.org.tw/main.php






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


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