Li's Group
Home
Research Papers
Teaching
Useful Links

Published Papers

I have published papers in the following four categories:

  • Methodology papers on Statistics and Econometrics

  • Conference papers on Machine Learning and Artificial Intelligence

  • Application papers at Other Fields, such as Geography

  • Others

List of Papers

  1. Zhu, Q., Li, W., Zhang, W. & Li, G. (2025+), Panel Quantile GARCH Models under Homogeneity, Journal of Business & Economic Statistics. In press

  2. Yuan, H., Lu, K. & Li, G. (2024+), Volatility Analysis with High-frequency and Low-frequency Historical Data, and Options-Implied Information, Statistica Sinica. In press.

  3. Zhang, X., Li, G., Liu, C.C. & Guo, J. (2024+), Tucker tensor factor models: Matricization and mode-wise PCA estimation, Scinece in China, Series A. In press.

  4. Huang, F., Lu, K., Zheng, Y. & Li, G. (2025), Supervised Factor Modeling for High-Dimensional Linear Time Series, Journal of Econometrics 249, 105995.

  5. Yang, M., Li, M. & Li, G. (2025), On memory-augmented gated recurrent unit network, International Journal of Forecasting 41, 844-858.

  6. Fang, Y., Jia, W., Cao, X., Jiang, P.-T., Li, G. & Chen, J. (2025), Proxy-Bridged Game Transformer for Interactive Extreme Motion Prediction, Proceedings of the International Conference on Computer Vision (ICCV-25). (The acceptance rate is 24%)

  7. Liu, Q., Zhao, W., Huang, W., Fang, Y., Yu, L. & Li, G. (2025), From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics, Proceedings of the 13th International Conference on Learning Representations (ICLR-25). (The acceptance rate is 32.08%)

  8. Wang, X., Wang, Z. & Li, G. (2025), Barriers to digital services trade: Evaluation of their restrictiveness with application of Brier Score, Oeconomia Copernicana 16, 197–245.

  9. Wang, D., Zheng Y. & Li, G. (2024), High-dimensional low-rank tensor autoregressive time series modeling, Journal of Econometrics 238, 105544.

  10. Yuan, H., Lu, K., Li, G. and Wang, J. (2024), High-Frequency-Based Volatility Model with Network Structure, Journal of Time Series Analysis 45, 533-557.

  11. Pei, Q., Qiu, M., Li, G., Wu, K.M., Mordechai, L., Liu, W. & Zhang, H. (2024), Cost of resilience to climate change: migration, conflicts, and epidemics in imperial China, Environmental Research Letters 19, 114025.

  12. Zhu, Q., Tan, S., Zheng, Y. & Li, G. (2023), Quantile autoregressive conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B 85, 1099-1127.

  13. Gao, Y., Zhu, X., Qi, H., Li, G., Zhang, R. & Wang, H. (2023), An asymptotic analysis of random partition based minibatch momentum methods for linear regression models, Journal of Computational and Graphical Statistics 32, 1083-1096.

  14. Zhang, X., Wang, D., Lian, H. & Li, G. (2023), Nonparametric quantile regression for homogeneity pursuit in panel data models, Journal of Business & Economic Statistics 41, 1238-1250.

  15. Zheng, Y., Wu, J., Li, W.K. & Li, G. (2023), Least absolute deviations estimation for nonstationary vector autoregressive time series models with pure unit roots, Statistics and Its Interface 16, 199-216.

  16. Huang, F., Lu, K., Cai, Y., Qin, Z., Fang, Y., Tian, G. & Li, G. (2023), Encoding recurrence into transformers, Proceedings of the 11th International Conference on Learning Representations (ICLR-23). (The acceptance rate is 31.8%, and this is an oral paper, i.e. notable-top-5%)

  17. Fang, Y., Cai, Y., Chen, J., Zhao, J., Tian, G. & Li, G. (2023), Cross-layer retrospective retrieving via layer attention, Proceedings of the 11th International Conference on Learning Representations (ICLR-23). (The acceptance rate is 31.8%)

  18. Pan, R., Ren, T., Guo, B., Li, F., Li, G. & Wang, H. (2022), A note on distributed quantile regression by pilot sampling and one-step updating, Journal of Business & Economic Statistics 40, 1691-1700.

  19. Zhu, Q. & Li, G. (2022), Quantile double autoregression, Econometric Theory 38, 793–839.

  20. Wang, D., Zheng, Y., Lian, H. & Li, G. (2022), High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association 117, 1338-1356. (GitHub)

  21. Wang, G., Zhu, K., Li, G. & Li, W.K. (2022), Hybrid Quantile Estimation for Asymmetric Power GARCH Models, Journal of Econometrics 227, 264-284.

  22. Zhang, Y., Lian, H., Li, G. & Zhu, Z. (2021), Functional additive quantile regression, Statistica Sinica 31, 1331-1351.

  23. Zhu, Q., Li, G. & Xiao, Z. (2021), Quantile Estimation of Regression Models with GARCH-X Errors, Statistica Sinica 31, 1261-1284.

  24. Cai, Y. & Li, G. (2021), A quantile function approach to the distribution of financial returns following TGARCH models, Statistical Modelling 21, 189–219.

  25. Zhao, J., Fang, Y. & Li, G. (2021), Recurrence along depth: deep convolutional neural networks with recurrent layer aggregation, Advances in Neural Information Processing Systems (NeurIPS 2021). Vol. 34, pp.10627-10640. (GitHub, the acceptance rate is 26%.)

  26. Tu, W., Liu, P., Liu, Y., Kong, L., Li, G., Jiang, B., Yao, H. & Jui, S. (2021), Nonsmooth Low-rank Matrix Recovery: Methodology, Theory and Algorithm, Proceedings of the Future Technologies Conference (FTC 2021), Vol. 1, pp 848–862.

  27. Li, D., Zeng, R., Zhang, L., Li, W.K. & Li, G. (2020), Conditional quantile estimation for hysteretic autoregressive models, Statistica Sinica 30, 809-824.

  28. Zhu, Q., Zeng, R. & Li, G. (2020), Bootstrap inference for GARCH models by the least absolute deviation estimation, Journal of Time Series Analysis 41, 21-40.

  29. Zhao, J., Huang, F., Lv, J., Duan, Y., Qin, Z., Li, G. & Tian, G. (2020), Do RNN and LSTM have long memory? Proceedings of the 37th International Conference on Machine Learning (ICML-20). Vol. 119, pp.11365-11375. (The acceptance rate is 21.8%.)

  30. Wang, D., Huang, F., Zhao, J., Li, G. & Tian, G. (2020), Compact autoregressive network, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). pp.6145-6152. (The acceptance rate is 20.6%)

  31. Zhang, D.D., Pei, Q., Lee, H.F., Jim, C.Y., Li, G., Zhang, M., Li, J., Wu, Z., Wang, L., Yue, R.P.H. & Zhang, S. (2020), Cultural dynamics of human resilience under climate change in Europe of past 2,500 years, Science of the Total Environment 744, 140842.

  32. Pei, Q., Li, G., Winterhalder, B.P. & Lowman, M. (2020), Regional patterns of pastoralist migrations under the push of reduced precipitation in imperial China, Global Ecology and Biogeography 29, 433-443.

  33. Dong, C., Li, G. & Feng, X. (2019), Lack-of-fit tests for quantile regression models, Journal of the Royal Statistical Society, Series B 81, 629-648. (GitHub)

  34. Liu, P., Tu, W., Zhao, J., Liu, Y., Kong, L., Li, G., Jiang, B., Tian, G. & Yao, H. (2019) M-estimation in low-rank matrix factorization: a general framework, Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM-19). pp. 568-577. (Regular paper with the acceptance rate of 9.08%).

  35. Tu, W., Yang, D., Kong, L., Che, M., Shi, Q., Li, G. & Tian, G. (2019), Ensemble-based Ultrahigh-dimensional Variable Screening, Proceedings of the 28th International Joint Conferences on Artifical Intelligence (IJCAI-19), pp. 3613-3619. (The acceptance rate is 17.8%)

  36. Pei, Q., Nowak, Z., Li, G., Xu, C. & Chan, W.K. (2019), The Strange Flight of the Peacock: Farmers’ atypical northwesterly migration from central China, 200BC-1400AD, Annals of the Association of American Geographers 109, 1583-1596. (The flagship journal of AAG)

  37. Wu, J., Li, G. & Xia, Q. (2018), Moment-based tests for random effects in the two-way error component model with unbalanced panels, Economic Modelling 74, 61-76.

  38. Zhu, Q., Zheng, Y. & Li, G. (2018), Linear double autoregression, Journal of Econometrics 207, 162-174.

  39. Zheng, Y., Zhu, Q., Li, G. & Xiao, Z. (2018), Hybrid quantile regression estimation for time series models with conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B 80, 975-993. (R codes)

  40. Zheng, Y., Li, W.K. & Li, G. (2018), A robust goodness-of-fit test for generalized autoregressive conditional heteroscedastic models, Biometrika 105, 73-89.

  41. Zhu, X., Pan, R., Li, G., Liu, Y. & Wang, H. (2017), Network vector autoregression, Annals of Statistics 45, 1096–1123.

  42. Li, G., Zhu, Q., Liu, Z. & Li, W.K. (2017), On mixture double autoregressive time series models, Journal of Business & Economic Statistics 35, 306-317.

  43. Zheng, Y., Li, Y. & Li, G. (2016), On Frechet autoregressive conditional duration models, Journal of Statistical Planning and Inference 175, 51-66.

  44. Lo, P.H., Li, W.K., Yu, P.L.H. & Li, G. (2016), On buffered threshold GARCH models, Statistica Sinica 26, 1555-1567.

  45. Pei, Q., Zhang, D.D., Li, G., Foret, P. & Lee, H.F. (2016), Temperature and precipitation effects on agrarian economy in late imperial China, Environmental Research Letters 11, 064008.

  46. Pei, Q., Zhang, D.D., Lee, H.F. & Li, G. (2016), Crop management as an agricultural adaptation to climate in early modern era: A comparative study of Eastern and Western Europe, Agriculture 6, 29.

  47. Zheng, Y., Li, Y., Li, W.K. and Li, G. (2016), Diagnostic checking for Weibull autoregressive conditional duration models. In: Li, W.K., Stanford, D.A., Yu, H. (editors): Advances in Time Series Methods and Applications: the A. Ian McLeod Festschrift, 107-114, Springer-Verlag, New York.

  48. Li, G., Guan, B., Li, W.K. & Yu, P.L.H. (2015), Hysteretic autoregressive time series models, Biometrika 102, 717-723.

  49. Li, M., Li, W.K. & Li, G. (2015), A new hyperbolic GARCH model, Journal of Econometrics 189, 428-436.

  50. Li, G., Li, Y. & Tsai, C.-L. (2015), Quantile correlations and quantile autoregressive modeling, Journal of the American Statistical Association 110, 246-261.

  51. Liu, S. & Li, G. (2015), Varying-coefficient mean-covariance regression analysis for longitudinal data, Journal of Statistical Planning and Inference 160, 89-106.

  52. Pei, Q., Zhang, D.D., Li, G. & Lee, H.F. (2015), Climate change and the macroeconomic structure in pre-industrial Europe: new evidence from wavelet analysis, PLoS ONE 10(6), e0126480.

  53. Pei, Q., Zhang, D.D., Li, G., Winterhalder, B. & Lee, H.F. (2015), Epidemics in Ming and Qing China: impacts of changes of climate andeconomic well-being, Social Science & Medicine 136-137, 73-80.

  54. Wu, J. & Li, G. (2014), Moment-based tests for individual and time effects in panel data models, Journal of Econometrics 178, 569-581.

  55. Li, D., Li, G. & You, J. (2014), Significant variable selection and autoregressive order determination for time series partially linear models, Journal of Time Series Analysis 35, 478-490.

  56. Li, G., Leng, C. & Tsai, C.-L. (2014), A hybrid bootstrap approach to unit root tests, Journal of Time Series Analysis 35, 299-321.

  57. Pei, Q., Zhang, D.D., Lee, H.F. & Li, G. (2014), Climate change and macro-economic cycles in pre-industrial Europe, PLoS ONE 9(2), e88155.

  58. Yu, P.L.H. and Li, G. (2014), Discussion on the paper “Principal volatility component analysis”, Journal of Business & Economic Statistics 32, 166-167.

  59. Li, M., Li, W.K. & Li, G. (2013), On mixture memory GARCH models, Journal of Time Series Analysis 34, 606-624.

  60. Pei, Q., Zhang, D.D., Li, G. & Lee, H.F. (2013), Short and long term impacts of climate variations on the agrarian economy in pre-industrial Europe, Climate Research 56, 169-180.

  61. Kwan, W., Li, W.K. & Li, G. (2012) On the estimation and diagnostic checking of the ARFIMA–HYGARCH model, Computational Statistics and Data Analysis 56, 3632-3644.

  62. Li, G. & Li, W.K. (2011), Testing a linear time series model against its threshold extension, Biometrika 98, 243-250.

  63. Li, M., Li, G. & Li, W.K. (2011), Score tests for hyperbolic GARCH models, Journal of Business & Economic Statistics 29, 579-586.

  64. Kwan, W., Li, W.K. & Li, G. (2011), On the threshold hyperbolic GARCH models, Statistics and Its Interface 4, 159-166.

  65. Li, G. & Li, W.K. (2009), Least absolute deviation estimation for unit root processes with GARCH errors, Econometric Theory 25, 1208-1227.

  66. Li, W.K. and Li, G. (2009), Discussion on the paper “Model selection for generalized linear models with factor-augmented predictors”, Applied Stochastic models in Business and Industry 25, 237-239.

  67. Li, W.K. and Li, G. (2009), Discussion on the paper “Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach”, Science in China, Series A 52, 1109-1110.

  68. Li, G. & Li, W.K. (2008), Testing for threshold moving average with conditional heteroscedasticity, Statistica Sinica 18, 647-665.

  69. Li, G. & Li, W.K. (2008), Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity, Biometrika 95, 399-414.

  70. Wang, H., Li, G. & Jiang, G. (2007), Robust regression shrinkage and consistent variable selection via the LAD-LASSO, Journal of Business & Economic Statistics 25, 347-355.

  71. Wang, H., Li, G. & Tsai, C.-L. (2007), Regression coefficients and autoregressive order shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B 69, 63-78.

  72. Li, G. & Li, W.K. (2005), Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach, Biometrika 92, 691-701.

  73. Pan, J., Li, G. & Xie, Z. (2002), Stationary solution and parametric estimation for bilinear model driven by ARCH noises, Science in China, Series A 45, 1523-1537.