报告题目：Non-standard inference for augmented double autoregressive models with null volatility coefficients
报 告 人：李东 清华大学统计学研究中心副教授
报告时间：2019年9月21日下午15：40-16：40
报告地点：数学楼 629
报告摘要：
This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Specifically, we find except for the Lagrange multiplier test statistic, asymptotics for both the Wald and the quasi-likelihood ratio test statistics are non-standard. In addition, a new portmanteau test based on self-weighted residuals is proposed with non-standard asymptotics. The entire procedure is valid as long as the data is stationary, and its usefulness is illustrated by simulation studies and one real example.
报告人简介：
李东，清华大学统计学研究中心副教授，2010年毕业于香港科技大学，2013年加入清华大学。主要研究兴趣：非线性时间序列分析，金融计量学，网络数据分析与大数据。目前担任全国工业统计学教学研究会常务理事，中国青年统计学家协会常务理事，中国现场统计研究会计算统计分会理事，北京应用统计学会理事。主持国际自然科学基金委面上项目2项，参与面上项目1项；结题青年基金项目1项。发表论文二十余篇，其中多篇论文发表在Journal of Econometrics, JBES, Econometric Theory, Biometrika等杂志上。
