报告题目：Generalising Dynamic Semiparametric Averaging Forecasting for Time Series with Discrete-valued Response and its Economic Applications
内容摘要
Dynamic discrete valued time series data exist in many economic applications, such as market price moving direction, credit scoring and others, 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 square 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 causal analysis of the UK road casualty data, which are shown to outperform many popular machine learning tools, including the random forest method, etc..
主讲人简介
卢祖帝，现为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、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，计量经济学和非线性时空数据分析及其统计机器学习和因果分析的动态建模等的挑战性统计和计量经济的理论方法及其在金融、气候、绿色金融和能源环境、卫生健康及工程应用等方面的研究。他是国际上非线性时空间数据统计学的主要研究者和倡导者之一。卢祖帝教授分别于 1991 年和 1996 年获中国科学院系统科学研究所统计学硕士和博士学位。他先后任职于东南大学，比利时鲁汶天主教大学，中国科学院数学与系统科学研究院，英国伦敦经济学院，澳大利亚科廷大学和阿德莱德大学及英国南安普顿大学。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和欧盟居里夫人研究基金项目及多项各种面上项目的支助。他是国际统计学会的当选会员。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 100 篇学术论文。他是国际杂志 Journal of Time Series Analysis，Environmental Modelling and Assessment 及 Research in Mathematics 和国内杂志《系统工程理论与实践》等的副主编，高级编辑或编委。
时间：2023年09月05日(周二)，16:30-18:00
地点：厦门大学经济楼D136
腾讯会议 ID：393 3774 3329
