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乔梦柯，博士就读于新加坡国立大学，信息系统与分析专业，研究方向为机器学习在社会科学方法中的应用，数据挖掘在金融会计领域的应用。
时间：9月22日（星期二）上午9:00—10:30
地点：腾讯会议，会议 ID：511 597 820
点击链接直接加入会议：https://meeting.tencent.com/s/XDslBq6B4eKr
讲座题目：Correcting Misclassification Bias in Regression Models with Variables Generated via Data Mining
讲座摘要：As a result of advances in data mining, more and more empirical studies in the social sciences apply classification algorithms to construct independent or dependent variables for further analysis via standard regression methods. In the classification phase of these studies, researchers need to subjectively choose a classification performance metric for optimization in the standard procedure. No matter which performance metric is chosen, the constructed variable still includes classification error because those variables cannot be classified perfectly. The misclassification of constructed variables will lead to inconsistent regression coefficient estimates in the following phase, which has been documented as a problem of measurement error in the econometrics literature. Yang et al. (2018) provided the pioneering discussions on the issue of estimation inconsistency due to misclassification in these studies. Our study attempts to investigate systematically the theoretical foundation of this problem when a newly constructed variable is utilized as the independent or dependent variable in linear and nonlinear regressions. Our theoretical analysis shows that consistent regression estimators can be recovered in all models studied in this paper. The main implication of our theoretical result is that researchers do not need to tune the classification algorithm to minimize the inconsistency of estimated regression coefficients because the inconsistency can be corrected by theoretical formulas, even when the classification accuracy is poor. Instead, we propose that a classification algorithm should be tuned to minimize the standard error of the focal regression coefficient derived based on the corrected formula. As a result, researchers can derive a consistent and most precise estimator in all models studied in this paper.
欢迎感兴趣的老师和同学参加。
人事与师资办公室
2020年9月15日