统计学系系列讲座之298期
时 间：2018年5月8日（周二）16:00-17:00
地 点：史带楼205室
主持人：张新生 教授 复旦大学管理学院统计学系
主 题：High Dimensional and Banded Vector Autoregressions
主讲人：Professor Qiwei Yao Department of Statistics，London School of Economics
简 介：Qiwei Yao 教授目前兼任复旦大学大数据学院学术委员会委员，大数据基础与技术研究所所长。他是Fellow of Institute of Mathematical Statistics，Elected member of International Statistical Institute，Fellow of American Statistical Association，Fellow of Royal Statistical Society。 他目前所从事的主要研究领域包括: Time series analysis; high-dimensional time series modelling and forecasting; dimension reduction and factor modelling; financial econometrics; nonparametric regression等
摘 要：We first consider a class of vector autoregressive models with banded coefficient matrices. The setting represents a type of sparse structure for high-dimensional time series, though the implied autocovariance matrices are not banded. The structure is also practically meaningful when the order of component time series is arranged appropriately. We then consider such a banded autoregression in the context of spatio-temporal modelling, for which the conventional least squares estimation is no longer valid. Instead we apply the least squares method based on a Yule-Walker equation to estimate autoregressive coefficient matrices. Illustration with both simulated and real data is presented.
统计学系
2018-5-7
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