时 间：2023年3月8日 15:00-16:00
地 点：理科大楼A1716
报告人：刘庆丰 教授 日本法政大学
主持人：於州 教授 华东师范大学
摘 要：
This study proposes a tying maximum likelihood estimation (TMLE) method to improve the performance of estimation of statistical and econometric models in which most time series have long sample periods, whereas the other time series are very short. The main idea of the TMLE is to tie the parameters of the long time series with those of the short time series together so that some useful information in the long time series which is related to the short time series can be transferred to the short time series. The information transferred from the long series can help improve the estimation accuracy of the parameters of the short series. We first provide asymptotic properties of the TMLE and show its finite-sample risk bound under a fixed tuning parameter which determines the strength of tying. Further, we provide a method for selecting the tuning parameter based on a fixed-design wild bootstrap procedure. A finite sample theory about this method is derived, which tells us how to conduct the bootstrap procedure effectively. Extensive artificial simulations and empirical applications show that the TMLE has an outstanding performance in point estimate and forecast.
报告人简介：
刘庆丰，日本法政大学工业与系统工程系教授。2007年获得日本京都大学经济学博士，2008年在美国普林斯顿大学金融与统筹学院做博士后研究。先后担任了日本京都大学经济研究所客座教授、美国哥伦比亚大学统计学院访问学者、日本小樽商科大学商学部经济学科教授。刘庆丰教授的研究领域为计量经济学，统计学，机器学习。其研究成果发表在Journal of Business & Economic Statistics，Econometrics Journal，Econometric Reviews，Mathematics and Computers in Simulation等多个国际专业期刊上，并于2017年获得国际管理工程师协会最佳论文奖。