[时间] 2023年8月7日周一，上午8:30—11:30，下午14:00—17:00
[地点] 线下电竞博彩
楼214（线上腾讯会议号748769543）
[主办方] 电竞博彩-国内电竞博彩
产业经济研究所 数量经济研究所
讲座人：卢祖帝，英国南安普顿大学数学科学学院和南安普顿统计科学研究所的统计学终身讲席教授
主题：时间序列的广义动态半参数平均预测方法及其在经济金融能源等方面的应用—从连续取值到离散取值响应变量
Abstract: Dynamic discrete valued time series data exist in many economic applications, but its research is still relatively rare compared with the rich research of continuous-valued time series in semiparametric modelling. In this paper, we propose to explore how to utilise the useful high dimensional dynamic lagged information for forecasting of time series data with discrete-valued response. Our approach will generalise the existing flexible semiparametric marginal regression model averaging (MARMA) forecasting of Li, Linton and Lu (2015), which has been shown a useful data-driven method, but was designed for nonlinear forecasting of continuous valued time series by a least squares averaging. We have hence suggested a generalised MARMA (GMARMA) procedure under a general time series conditional exponential family of distributions, which flexibly accommodates nonlinear forecasting of discrete-valued response, and further allowing the lagged effects including discrete-valued information for forecasting. A conditional likelihood model averaging method, instead of the least squares, is thus developed for the averaging weights estimation in the GMARMA, under beta-mixing time series data generating process with asymptotic normality established. Furthermore, an adaptively penalised GMARMA (PGMARMA) is suggested to select the important variables for an improved forecasting. The oracle properties of the PGMARMA weights are established as if the true non-zero weights were known. These procedures are further supported by Monte Carlo simulations and empirical applications to forecasting of the FTSE 100 index market moving direction and the UK road casualty data, which are shown to outperform many popular machine learning tools, including the random forest method, etc..
This is a joint work with Rong Peng and Fangsheng Ge.
讲座人中文简介：
卢祖帝，现为英国南安普顿大学(英国罗素大学集团 Russell Group 的创始成员)数学科学学院和南安普顿统计科学研究所的统计学终身讲席教授(Chair in Statistics)、博士生导师。他目前的主要研究兴趣为非线性时间序列分析、金融统计、计量经济学、非线性时空大数据分析及智能化机器学习和因果分析的动态建模等领域的挑战性的统计和计量经济的理论与方法,及其在金融、气候、绿色金融和能源环境及工程应用等方面的研究。他是国际上非线性时空数据统计学的主要研究者和倡导者之一。卢祖帝教授于 1996 年获中国科学院系统科学研究所统计学博士学位。他先后任职于中国南京东南大学(1991-1993)、比利时鲁汶天主教大学(1996-1997)、中国科学院数学与系统科学研究院(1997-2003)、英国伦敦经济学院(2003-2006)、澳大利亚科廷大学(2006-2009)和阿德莱德大学(2006-2013,澳洲著名大学八校联盟成员)及英国南安普顿大学(2013-至今)。曾先后获得中国国家自然科学重点基金、澳大利亚国家研究理事会未来研究杰出青年基金项目(Australian Future Fellow,对应于中国国家杰出青年基金)和欧盟居里夫人研究基金项目 (Career Integration Grant/Marie Curie Fellow)及多项各种面上项目的资助。他是国际统计学会的当选会员(elected member)。已在国际统计学和计量经济学的主要杂志包括顶级期刊 Annals of Statistics, Journal of American Statistician Association, Journal of Royal Statistical Society series B, Journal of Econometrics, Econometric Theory 等发表 90 多篇学术论文。他是国际杂志《Journal of Time Series Analysis》,《Environmental Modelling and Assessment》及《Cogent Research in Mathematics and Statistics》(负责统计版块)和中国国内杂志《系统工程理论与实践》等的副主编、高级编辑或国外编委。
