Time: 15:30-16:30pm, Nov. 22th, 2018, Monday
Venue: RoomA1716, Science Building, North Zhongshan Road Campus
Spreker: Full Professor Rongmao Zhang, ZheJiang University
Abstract: Cointegration inference is often built on the correct specification for the short-run dynamic vector autoregression. However, this specification is unknown a priori. A too small lag length leads to erroneous inference due to misspecification, while using too many lags leads to dramatic increase in the number of parameters, especially when the dimension of time series is high. In this paper, we develop a new methodology which adds an error correction term for long-run equilibrium to a latent factor model for modeling short-run dynamic relationship. Two eigenanalysis based methods for estimating, respectively, cointegration and latent factor process consist of the cornerstones of the inference. The proposed error correction factor model does not require to specify the short-run dynamics explicitly, and is particularly effective for high-dimensional cases when the standard error-correction suffers from overparametrization. It also increases the predictability over a pure factor model. Asymptotic properties of the proposed methods are established when the dimension of the time series is either fixed or diverging slowly as the length of time series goes to infinity. Illustration with both simulated and real data sets is also reported.
Speaker’s Bio：张荣茂，浙江大学教授，博导，2004年在浙江大学获得博士学位，2004年7月-2006年6月在北京大学从事博士后研究，2006年至今在浙江大学工作，多次访问香港科大、香港中文大学和伦敦政治经济学院。主要从事非平稳时间序列和高维空间数据的理论与应用研究，在国际重要SCI/SSCI杂志发表论文近40篇，发表杂志包括Ann. Statist.，J. Amer. Assoc. Statist.， J. Econometrics等。2015年获浙江省杰出青年基金，主持国家自然科学基金和省部级基金项目多项。现任浙大统计所副所长，数据科学中心兼职教授，浙江省现场统计研究所副理事长，J. Korean Statist. Soc.（SCI期刊）和Intern. J. Math. Statist.编委。
