Research Papers

Click here for the full list of publications.

Recent Manuscripts

1. Q Liu, W Zhao, W Huang, Y Fang, L Yu & G Li (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%)

2. X Zhang, D Wang, G Li & D Sun (2024), Robust and Optimal Tensor Estimation via Robust Gradient Descent,
   Submitted.

3. W Li, Y Lin, Q Zhu & G Li (2024), An efficient multivariate volatility model for many assets,
   Submitted.

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

5. H Yuan, K Lu, Y Guo & G Li (2024), HAR-Ito models and high-dimensional HAR modeling for high-frequency data,
   Submitted.

6. Y Si, Y Zhang, Y Cai & G Li (2024), An efficient tensor regression for high-dimensional data,
   Submitted.

7. D Wang, X Zhang, G Li & R Tsay (2024), High-dimensional vector autoregression with common response and predictor factors,
   Submitted.

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

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

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

11. F Huang, K Lu, Y Cai, Z Qin, Y Fang, G Tian & G Li (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%)

12. Y Fang, Y Cai, J Chen, J Zhao, G Tian & G Li (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%)

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

14. J Zhao, Y Fang & G Li (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%.)

15. J Zhao, F Huang, J Lv, Y Duan, Z Qin, G Li & G Tian (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%.)

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

Selected Publications in the Past

1. G Wang, K Zhu, G Li & WK Li (2022), Hybrid quantile estimation for asymmetric power GARCH models,
   Journal of Econometrics 227, 264-284.

2. Q Pei, Z Nowak, G Li, C Xu & WK Chan (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 the Association of American Geographers, just like JASA in statistics.)

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

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

5. Y Zheng, Q Zhu, G Li & Z Xiao (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)

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

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

8. G Li, B Guan, WK Li & PLH Yu (2015), Hysteretic autoregressive time series models,
   Biometrika 102, 717-723.

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

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

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

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

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

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

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