报告题目：Lasso and Post-Lasso Inference for Multiple Threshold Regressions with an Application to Return Predictability
内容摘要：This paper considers a multiple threshold regression model, where the coefficient parameters can switch between regimes according to the value of a threshold variable, and establishes the valid inference of a Lasso-type shrinkage estimation procedure that consistently estimates the multiple thresholds. The procedure is robust to both diverging number of thresholds and shrinking threshold effects. Asymptotic properties, including the consistency of the group Lasso estimators and threshold number estimator, and limiting distribution of the threshold estimators and the likelihood ratio statistic, are established under a set of regularity conditions. The focus is further placed on the new development of the post-Lasso inferential theory, which accounts for the randomness of threshold selection and is achieved by characterizing the distribution of the coefficient estimators conditional on the selected model. Monte Carlo simulations demonstrate that the estimators are well-behaved in finite samples. An empirical application to return prediction further illustrates the practical merits of our methodology.
主讲人简介：马辰辰，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。2024年于北京大学获得统计学博士学位。她主要从事于计量经济学相关领域的理论和应用研究工作，研究方向包括时间序列分析，高维数据分析，因子模型，机器学习方法等。她的研究工作发表在计量经济学国际顶级期刊《Journal of Econometrics》等。
报告时间：2024年11月5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：541 966 714
