李东

李东

副教授

研究方向：时序计量经济学，金融计量经济学，非线性时间序列分析，网络与大数据

地址：清华大学伟清楼203-B室

电话：010-62780177

邮箱：malidong@tsinghua.edu.cn

工作经历
  • 2016/12-现今，清华大学统计学研究中心，副教授
  • 2015/10-2016/12, 清华大学统计学研究中心，助理教授
  • 2013/09-2015/10, 清华大学丘成桐数学科学中心，助理教授
教育背景
  • 博士 香港科技大学 (2010)
  • 硕士 中科院数学与系统科学研究院 (2005)
  • 学士 曲阜师范大学 (2002)
访问经历
  • 2019/07-2019/08，香港大学，访问学者
  • 2018/07-2018/08，香港大学，访问学者
  • 2017/07-2017/08，香港大学，访问学者
  • 2015/10-2015/10，香港科技大学，访问学者
  • 2013/02-2013/08，香港科技大学，访问学者
  • 2012/05-2012/05，英国伦敦经济与政治学院，访问学者
  • 2011/08-2013/02，美国爱荷华大学，博士后
  • 2011/02-2011/07，香港科技大学，博士后
  • 2005/09-2006/05，香港科技大学，研究助理
研究兴趣
  • 时序计量经济学
  • 金融计量经济学
  • 非线性时间序列分析
  • 网络与大数据分析
发表论文
  • Luo, D., Zhu, K., Gong, H. and Li, D.* (2021+). Testing error distribution by kernelized Stein discrepancy in multivariate time series models. Journal of Business & Economic Statistics.
  • Jiang, F.Y., Li, D., Li, W.K. and Zhu, K. (2021+). Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica.
  • Li, D., Li, M. and Zeng, L. (2021+). Simulation and application of subsampling for threshold autoregressive moving-average models, Communications in Statistics: Simulation and Computation.
  • Sun, L.Y. and Li, D.* (2021). Change-point detection for expected shortfall in time series. Journal of Management Science and Engineering 6, 324-335.
  • Jiang, F.Y., Li, D. and Zhu, K. (2021). Adaptive inference for a semiparametric GARCH model. Journal of Econometrics 224, 306-329.
  • Jiang, F.Y., Li, D. and Zhu, K. (2020). Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics 215, 165-183.
  • Li, D. and Tong, H. (2020). On an absolute autoregressive model and skew symmetric distributions. Statistica 80, 177-198.
  • Zhou, J., Li, D., Pan, R. and Wang, H. S. (2020). Network GARCH model. Statistica Sinica 30, 1723-1740.
  • Gong, H. and Li, D.* (2020). On the three-step non-Gaussian quasi-maximum likelihood estimation of heavy-tailed double AR models. Journal of Time Series Analysis 41, 883-891.
  • Li, D.* and Qiu, J.M. (2020). The marginal density of a TMA (1) process. Journal of Time Series Analysis 41, 476-484.
  • Yang, Y. and Li, D.* (2020). Self-weighted LAD-based inference for heavy-tailed continuous threshold autoregressive models. Journal of Time Series Analysis 41,163-172.
  • Li, D. and Zhu, K. (2020). Inference for asymmetric exponentially weighted moving average models. Journal of Time Series Analysis 41,154-162.
  • Guo, S., Li, D. and Li, M.Y. (2019). Strict stationarity testing and GLAD estimation of double autoregressive models. Journal of Econometrics 211, 319-337.
  • Li, D., Guo, S. and Zhu, K. (2019). Double AR model without intercept: An alternative to modeling nonstationarity and heteroscedasticity. Econometric Reviews 38, 319-331.
  • Li, D., Ling, S., Tong, H. and Yang, G.R. (2019). On Brownian motion approximation of compound Poisson processes with applications to threshold models. Advances in Decision Sciences 23. Bridging.pdf
  • Li, D. and Wu, W. (2018). Renorming volatilities in a family of GARCH models. Econometric Theory 34, 1370-1382.
  • Liu, F., Li, D.* and Kang, X.M. (2018). Sample path properties of an explosive double AR model. Econometric Reviews 37, 484-490.
  • Li, D., Zhang, X., Zhu, K. and Ling, S. (2018). The ZD-GARCH model: A new way to study heteroscedasticity. Journal of Econometrics 202, 1-17.
  • Li, D. and Tong, H. (2016). Nested sub-sample search algorithm for estimation of threshold models. Statistica Sinica 26, 1543-1554.
  • Li, D., Ling, S. and Zhang, R.M. (2016). On a threshold double autoregressive model. Journal of Business & Economic Statistics 34, 68-80.
  • Li, D., Ling, S. and Zakoïan, J.-M. (2015). Asymptotic inference in multiple-threshold double autoregressive models. Journal of Econometrics 189, 415-427.
  • Li, D., Li, M. and Wu, W. (2014). On dynamics of volatilities in nonstationary GARCH models. Statistics and Probability Letter 94, 86-90.
  • Chen, M., Li, D.* and Ling, S. (2014). Non-stationarity and quasi-maximum likelihood estimation on a double autoregressive model. Journal of Time Series Analysis 35, 189-202.
  • Chan, K.S., Li, D., Ling, S. and Tong, H. (2014). On conditionally heteroscedastic AR models with thresholds. Statistica Sinica 24, 625-652.
  • Li, D. (2014). Weak convergence of the sequential empirical processes of residuals in TAR models. Science China: Mathematics 57, 173-180.
  • Li, D., Chan, K.S. and Schilling, K.E. (2013). Nitrate concentration trends in Iowa’s rivers, 1998 to 2012: What challenges await nutrient reduction initiatives? Journal of Environmental Quality 42, 1822-1828.
  • Li, D., Ling, S. and Li, W. K. (2013). Asymptotic theory on the least squares estimation of threshold moving-average models. Econometric Theory 29, 482-516.
  • Wu, W., Li, D., Pan, S. and Chen, M. (2013) Three-regime mean reversion, TAR and its applications. Systems Engineering - Theory & Practice 33, 901-909.
  • Li, D. (2012). A note on moving-average models with feedback. Journal of Time Series Analysis 33, 873-879.
  • Li, D., Ling, S. and Tong, H. (2012). On moving-average models with feedback. Bernoulli 18, 735-745.
  • Li, D. and Ling, S. (2012). On the least squares estimation of multiple-regime threshold autoregressive models.  Journal of Econometrics 167, 240-253
  • Li, D., Li, W. K. and Ling, S. (2011). On the least squares estimation of threshold autoregressive and moving-average models. Statistics and Its Interface 4, 183-196.
  • Ling, S. and Li, D. (2008). Asymptotic inference for a non-stationary double AR(1) model. Biometrika 95, 257-263.
  • Ling, S., Tong, H. and Li, D. (2007). Ergodicity and invertibility of threshold moving-average models. Bernoulli 13, 161-168.
在审论文
  • Zhang, X., Li, D. and Tong, H. (2021). On the LSE of k-threshold-variable AR models.
  • Zhang, X. and Li, D.* (2021). Smooth transition moving average models: Estimation, testing and computation.
  • Yang, X. and Li, D.* (2021). Estimation of the empirical risk-return relation: A generalized-risk-in-mean model.
  • Yu, C., Li, D. and Li, G.R. (2021). Simultaneous confidence bands for nonparametric Berkson errors-in -variables models
  • Li, D., Tao, Y., Yang, Y. and Zhang, R.M. (2021). Maximum likelihood estimation for α-stable DAR models.
教  学
  • 研究生课程
  • 《时间序列分析》(2017/Spring)
  • 《高等概率论 I》 (2016-21/Fall)
  • 《多元统计分析》 (2014,2015/Spring)
  • 《高等统计(学)》 (2014/Fall)
  • 本科生课程
  • 《应用时间序列分析》(2017,2018,2020,2022/Spring)
  • 《初等概率论》 (2016/Fall)
  • 《统计学引论》(2018/Fall, 2020-22/Spring with Dr. K. Deng)
  • 《金融统计》 (2017, 2019/Spring)
  • 《多元统计分析》（2021/Spring）

毕业学生

  • 龚   欢 (2020, 清华大学优秀博士学位论文奖)
  • 蒋斐宇 (2021, 第25届清华大学研究生“学术新秀”称号[共10人])
社会服务
  • 北京大数据协会常务理事
  • 北京应用统计学会理事
  • 中国现场统计研究会计算统计分会理事
  • 全国工业统计学教学研究会常务理事
  • 全国工业统计学教学研究会数字经济与区块链技术协会常务理事
  • 中国青年统计学家协会常务理事
  • 中国概率统计学会副秘书长
组织会议
  • Co-organizer, the international conference on Complex Time Series Modelling and Forecasting: Dynamic Network, Spatio-temporal Data, and Functional Processes, Tsinghua-Sanya International Mathematics Forum, Jan. 8-12, 2018. (with Professor Marc Genton at KAUST, Professor Eric D. Kolaczyk at Boston University, and Professor Qiwei Yao at the LSE)
  • Organizer, Mini workshop on Big Data and Internet Finance, Tsinghua University, Dec. 18, 2016.
  • Co-organizer, 2016 Tsinghua Symposium on Statistics and Data Science for Young Scholars, Tsinghua University, Dec. 9-11, 2016. (with Ke Deng and Lin Hou)
  • Co-organizer, the international conference on Time Series Econometrics, Tsinghua-Sanya International Mathematics Forum, Dec. 18-20, 2015. (with Professor Shiqing Ling at HKUST and Professor Chuanzhong Chen at Hainan Normal University)
杂志审稿
  • Applied Stochastic Models in Business and Industry，Annals of Statistics，Biometrika，Colombian Journal of Statistics，Communications in Statistics - Simulation and Computation，Computational Statistics & Data Analysis，European Journal of Industrial Engineering，Econometric Theory，Journal of Econometrics，Journal of the Korean Statistical Society，Journal of Risk and Financial Management，Metrika，Statistica Sinica，Stochastic Environmental Research and Risk Assessment，Statistics & Probability Letters，
在研项目
  • 国家自然科学基金面上项目，"带有外生变量的非线性非平稳时间序列模型的统计推断及应用"，主持，2020/01-2023/12.
  • 国家自然科学基金面上项目，“高维时间序列的网络分析”，主持，2018/01-2021/12.
  • 国家自然科学基金面上项目，“时间序列分析中几种假设检验问题的研究”，参与，2016/01-2019/12.
  • 国家自然科学青年基金，“带有稳定新息的条件异方差模型的统计推断及其应用”, 主持，2015/01-2017/12.

个人简历：CV

Mathematics Genealogy
(https://genealogy.math.ndsu.nodak.edu/id.php?id=264334&fChrono=1)

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  • Essentially, all models are wrong, but some are useful. —— Box, G. P.
  • When solving a given problem, try to avoid solving a more general problem as an intermediate step. —— Vapnik, V.