報告時間：2023年6月16日（星期五）10:00-11:30
報告地點：管理學院1125會議室
報 告 人：喬夢柯 特任副教授
工作單位：中國科學技術大學
舉辦單位：管理學院
報告簡介：
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. The pioneering discussions on the issue of estimation inconsistency because of misclassification in these studies have been provided. Our study attempts to investigate systematically the theoretical foundation of this problem when a newly constructed variable is used 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.
報告人簡介：
喬夢柯，中國科學技術大學管理學院特任副教授，先后畢業于新加坡國立大學（博士）、華中科技大學大學（學士）。曾在國內外知名期刊和學術會議上發表論文包括Information Systems Research， International Conference on Information Systems，Workshop on Information Technologies and Systems。研究方向主要是機器學習與因果推斷，文本挖掘，計量經濟學等。
