Dong Li

Dong Li

Dong Li

Associate Professor

Research Areas:financial econometrics, nonlinear time series analysis, network and big data

Office: Room 212-C, Weiqing Building

Phone: +86-10-62780177

Email: malidong@tsinghua.edu.cn

Academic Position
  • Associate Professor, Center for Statistical Science, Tsinghua University, 01/2017- present.
  • Assistant Professor, Center for Statistical Science, Tsinghua University, 11/2015 – 12/2016.
  • Assistant Professor, Yau Mathematical Sciences Center, Tsinghua University, 09/2013 – 10/2015.
Education
  • Hong Kong University of Science and Technology, Hong Kong (2010)
  • Academy of Mathematics and Systems Science, Chinese Academy of Sciences (2005)
  • Qufu Normal University (2002)
Research and Visiting Experience
  • 2015/10-2015/10, Hong Kong University of Science and Technology, Visiting Scholar.
  • 2013/02-2013/08, Hong Kong University of Science and Technology, Visiting Scholar.
  • 2012/05-2012/05, London School of Economics & Political Science, Visiting Scholar.
  • 2011/08-2013/02, University of Iowa, Post-doc Fellow.
  • 2011/02-2011/07, Hong Kong University of Science and Technology, Post-doc Fellow.
  • 2005/09-2006/05, Hong Kong University of Science and Technology, Research Assistant.
Research Interests
  • Financial Econometrics
  • Nonlinear Time Series Analysis
  • Network and Big Data
Publications
  • Li, D., Guo, S. and Zhu, K. (2017). A double AR model without intercept: an alternative to modeling nonstationarity and heteroscedasticity. Econometric Reviews. (accepted)
  • Liu, F., Li, D. and Kang, X.M. (2017). Sample path properties of an explosive double AR model. Econometric Reviews. (accepted)
  • 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.
Submitted Paper
  • Li, D. and Wu, W. (2017). Renorming volatilities in a family of GARCH models.
  • Li, D., Ling, S. and Zhu, K. (2017). ZD-GARCH model: a new way to study heteroscedasticity.
  • Li, D. and Zhu, K. (2017). A new ZD-GJR model for asymmetric and heavy-tailed nonstationarity.
  • Li, D., Ling, S. and Yang, G. R. (2017). Bridging compound Poisson processes and Brownian motions with applications to threshold models.
  • Chow, J., Li, D., Pan, R. and Wang, H. S. (2017). Network GARCH model for estimating stock price volatility.
Teaching
  • Graduate courses
  • Time Series Analysis. (2017/Spring)
  • Advanced Probability Theory I. (2016/Fall)
  • Multivariate Statistical Analysis. (2014,2015/Spring)
  • Advanced Mathematical Statistics (2014/Fall)
  • Undergraduate courses
  • Applied Time Series Analysis. (2017/Spring)
  • Elementary Probability Theory. (2016/Fall)
Service
  • Council member of Beijing Applied Statistic Association
Organizing Conference
  • 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)
  • 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)
  • Organizer, Mini workshop on Big Data and Internet Finance, Tsinghua University, Dec. 18, 2016.
Journal Reviewing
  • Applied Stochastic Models in Business and IndustryAnnals of StatisticsBiometrikaColombian Journal of StatisticsCommunications in Statistics – Simulation and ComputationComputational Statistics & Data AnalysisEuropean Journal of Industrial EngineeringEconometric TheoryJournal of EconometricsJournal of the Korean Statistical SocietyJournal of Risk and Financial ManagementMetrikaStatistica SinicaStochastic Environmental Research and Risk AssessmentStatistics & Probability Letters

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  • Curriculum Vitae:cv.pdf

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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.