报告题目：Improve Machine Learning by Model Averaging

报 告 人：张新雨研究员 中科院系统所

报告时间：2020年6月12日 下午 13:30-14:30

报告地点：腾讯会议 ID：852 522 267

密码: 200612

或点击链接直接加入会议：

https://meeting.tencent.com/s/fXflS3aHQCQd

校内联系人：赵世舜 zhaoss@jlu.edu.cn

报告摘要：

This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and the simple average method.

报告人简介：

张新雨，中科院系统所研究员，Texas A&M大学博士后、Penn State 大学Research Fellow。主要研究方向为模型平均、模型选择、组合预测等。国家杰出青年科学基金获得者，主持3项国家自然科学基金，目前担任《JSSC》、《SADM》、《系统科学与数学》、《应用概率统计》编委和《Econometrics》客座主编。