第653期岭南学术论坛（金融学系列Seminar）

语 言            中文+英文

报告摘要：

This paper proposes a new class of time series models, the functional quantile autoregression (FQAR) models, in which the conditional distribution of the observation at the current time point is affected by its past distributional information, and expressed as a functional of the past conditional quantile functions. The model can capture systematic influences of the past distributional information on the current distribution, and therefore constitute a significant extension of traditional time series models in which the effect of conditioning information is confined to only a few selected characteristics of the past distribution. We propose a sieve estimator for the model. The asymptotic properties and finite sample performance of this model are investigated.

报告人介绍：

       Zhijie Xiao教授，2004年至今任教于美国波士顿学院经济学系，耶鲁大学经济学博士，现担任Econometric Theory, Econometrics Reviews, Economics Letters,  Economics Bulletin, Journal of Time Series Econometrics, Journal of Risk and Financial Management, Econometrics Journal, Journal of American Statistical Association 等杂志副主编；在包括Econometrica, Journal of Econometrics， Journal of American Statistical Association等国际顶级统计学和经济学期刊发表文章80多篇；获Multa Scripsit Award in Econometric Theory，Cowles Foundation for Research in Economics，Boston College Distinguished Junior Scholar Research Award等多项奖励。

       欢迎感兴趣的师生参加！

       中山大学岭南学术论坛分经济学和金融学两大系列，是定期邀请国内外优秀学者前来开展学术交流的平台。每系列每个月定期举办2-3次。目前已经成功举办多期，并得到了各界的高度评价。