報告題目：Improve Machine Learning by Model Averaging
報 告 人：張新雨研究員 中科院系統所
報告時間：2020年6月12日 下午 13:30-14:30
報告地點：騰訊會議 ID：852 522 267
密碼: 200612
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https://meeting.tencent.com/s/fXflS3aHQCQd
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
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.
報告人簡介：
張新雨，中科院系統所研究員，Texas A&M大學博士後、Penn State 大學Research Fellow。主要研究方向為模型平均、模型選擇、組合預測等。國家傑出青年科學基金獲得者，主持3項國家自然科學基金，目前擔任《JSSC》、《SADM》、《系統科學與數學》、《應用概率統計》編委和《Econometrics》客座主編。
