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AR-SIEVE BOOTSTRAP FOR HIGH-DIMENSIONAL TIME SERIES
时间:2022年07月18日 14:13 点击数:

报告人:毕达宁

报告地点:腾讯会议ID:437 455 172

报告时间:2022年07月25日星期一10:00-11:00

邀请人:胡江

报告摘要:

This paper proposes a new AR-sieve bootstrap approach on high-dimensional time series. The major challenge of classical bootstrap methods on high-dimensional time series is two-fold: the curse of dimensionality and temporal dependence. To tackle such difficulty, we utilise factor modelling to reduce dimension and capture temporal dependence simultaneously. A factor-based bootstrap procedure is constructed, which conducts an AR-sieve bootstrap on the extracted low-dimensional common factor time series and then recovers the bootstrap samples for original data from the factor model. Asymptotic properties for bootstrap mean statistics and extreme eigenvalues are established. Various simulations further demonstrate the advantages of the new AR-sieve bootstrap under high-dimensional scenarios. Finally, an empirical application on particulate matter (PM) concentration data is studied, where bootstrap confidence intervals for mean vectors and autocovariance matrices are provided.

会议密码:2022

主讲人简介:

毕达宁,澳大利亚国立大学统计学博士,现任湖南大学金融与统计学院助理教授,主要研究兴趣包括高维统计方法,时间序列分析,精算风险模型等。目前担任中国现场统计研究会风险管理与精算分会理事,同时是统计学SCI期刊《Statistics》的匿名审稿人。

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