报告题目：Machine Learning with Nonstationary Data
内容摘要：Machine learning offers a promising set of tools for forecasting. However, some of the well-known properties do not apply to nonstationary data. I propose a simple procedure to extend machine learning methods to nonstationary data that does not require the researcher to have prior knowledge of which variables are non-stationary or the nature of the nonstationarity. I illustrate theoretically that using this procedure with LASSO or adaptive LASSO generates consistent variable selection on a mix of stationary and nonstationary explanatory variables. In a related paper, this approach was also shown to result in consistent estimation of principal components with the presence of nonstationarity. In an empirical exercise, I examine the success of this approach at forecasting U.S. inflation rates and the industrial production index using a number of different machine learning methods. I find that the proposed method either significantly improves prediction accuracy over traditional practices or delivers comparable performance, making it a reliable choice for obtaining stationary components of high-dimensional data. Another application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.
主讲人简介：奚晋，中国科学院数学与系统科学研究院预测科学研究中心助理研究员。本科就读于北卡罗莱纳大学教堂山分校，获得经济学与数学双学位。2024年于加州大学圣地亚哥分校获得经济学博士学位。她的研究领域是计量经济学，主要涉及的研究领域包括机器学习的预测方法、高维非平稳时间序列、因子模型、政策学习、以及机制设计。
报告时间：2024年09月10日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：796 451 039
