- Yuan, H., Lu, K. and Li, G. (2023+) Volatility Analysis with High-frequency and Low-frequency Historical Data, and Options-Implied Information,
*Statistica Sinica*. In press. - Wang, D., Zheng, Y. and Li, G. (2023+), High-dimensional low-rank tensor autoregressive time series modeling,
*Journal of Econometrics*. In press. - Yuan, H., Lu, K., Li, G. and Wang, J. (2023+) High-Frequency-Based Volatility Model with Network Structure,
*Journal of Time Series Analysis*. In press. - Zhu, Q., Tan, S., Zheng, Y. and Li, G. (2023), Quantile autoregressive conditional heteroscedasticity,
*Journal of the Royal Statistical Society, Series B*85, 1099-1127. - Gao, Y., Zhu, X., Qi, H., Li, G., Zhang, R. and 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. - Zhang, X, Wang, D., Lian, H and Li, G. (2023) Nonparametric quantile regression for homogeneity pursuit in panel data models,
*Journal of Business & Economic Statistics*41, 1238-1250. - Zheng, Y., Wu, J., Li, W. K. and 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. - Pan, R., Ren, T., Guo, B., Li, F., Li, G. and Wang, H. (2022) A note on distributed quantile regression by pilot sampling and one-step updating,
*Journal of Business & Economic Statistics*40, 1691-1700. - Zhu, Q. and Li, G. (2022) Quantile double autoregression,
*Econometric Theory*38, 793–839. - Wang, D., Zheng, Y., Lian, H. and Li, G. (2022) High-dimensional vector autoregressive time series modeling via tensor decomposition,
*Journal of the American Statistical Association*117, 1338-1356. (arXiv; GitHub) - Wang, G., Zhu, K., Li, G. and Li, W.K. (2022) Hybrid Quantile Estimation for Asymmetric Power GARCH Models,
*Journal of Econometrics*227, 264-284. - Zhang, Y., Lian, H., Li, G. and Zhu, Z. (2021) Functional additive quantile regression,
*Statistica Sinica*31, 1331-1351. - Zhu, Q., Li, G. and Xiao, Z. (2021) Quantile Estimation of Regression Models with GARCH-X Errors,
*Statistica Sinica*31, 1261-1284. - Cai, Y. and Li, G. (2021) A quantile function approach to the distribution of
ﬁnancial returns following TGARCH models,
*Statistical Modelling*21, 189–219. - Li, D., Zeng, R., Zhang, L., Li, W.K. and Li, G. (2020) Conditional quantile estimation for hysteretic autoregressive models,
*Statistica Sinica*30, 809-824. - Zhu, Q., Zeng, R. and Li, G. (2020) Bootstrap inference for GARCH models by the least absolute deviation estimation,
*Journal of Time Series Analysis*41, 21-40. - Dong, C., Li, G. and Feng, X. (2019) Lack-of-fit tests for quantile regression models,
*Journal of the Royal Statistical Society, Series B*81, 629-648. (GitHub) - Wu, J., Li, G. and Xia, Q. (2018) Moment-based tests for random effects in the two-way error component model with unbalanced panels,
*Economic Modelling*74, 61-76. - Zhu, Q., Zheng, Y. and Li, G. (2018) Linear double autoregression,
*Journal of Econometrics*207, 162-174. - Zheng, Y., Zhu, Q., Li, G. and 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 code) - Zheng, Y., Li, W.K. and Li, G. (2018) A robust goodness-of-fit test for generalized autoregressive conditional heteroscedastic models,
*Biometrika*105, 73-89. - Zhu, X., Pan, R., Li, G., Liu, Y. and Wang, H. (2017) Network vector autoregression,
*Annals of Statistics*45, 1096–1123. - Li, G., Zhu, Q., Liu, Z. and Li, W.K. (2017) On mixture double autoregressive time series models,
*Journal of Business & Economic Statistics*35, 306-317. - Zheng, Y., Li, Y. and Li, G. (2016) On Frechet autoregressive conditional duration models,
*Journal of Statistical Planning and Inference*175, 51-66. - Lo, P.H., Li, W.K., Yu, P.L.H. and Li, G. (2016) On buffered threshold GARCH models,
*Statistica Sinica*26, 1555-1567. - Li, G., Guan, B., Li, W.K. and Yu, P.L.H. (2015) Hysteretic autoregressive time series models,
*Biometrika*102, 717-723. - Li, M., Li, W.K. and Li, G. (2015) A new hyperbolic GARCH model,
*Journal of Econometrics*189, 428-436. - Li, G., Li, Y. and Tsai, C.-L. (2015) Quantile correlations and quantile autoregressive modeling,
*Journal of the American Statistical Association*110, 246-261. - Liu, S. and Li, G. (2015) Varying-coefficient mean-covariance regression analysis for longitudinal data,
*Journal of Statistical Planning and Inference*160, 89-106. - Wu, J. and Li, G. (2014) Moment-based tests for individual and time effects in panel data models,
*Journal of Econometrics*178, 569-581. - Li, D., Li, G. and You, J. (2014) Significant variable selection and autoregressive order determination for time series partially linear models,
*Journal of Time Series Analysis*35, 478-490. - Li, G., Leng, C. and Tsai, C.-L. (2014) A hybrid bootstrap approach to unit root tests,
*Journal of Time Series Analysis*35, 299-321. - Li, M., Li, W.K. and Li, G. (2013) On mixture memory GARCH models,
*Journal of Time Series Analysis*34, 606-624. - Kwan, W., Li, W.K. and Li, G. (2012) On the estimation and diagnostic checking of the ARFIMA–HYGARCH model,
*Computational Statistics and Data Analysis*56, 3632-3644. - Li, G. and Li, W.K. (2011) Testing a linear time series model against its threshold extension,
*Biometrika*98, 243-250. - Li, M., Li, G. and Li, W.K. (2011) Score tests for hyperbolic GARCH models,
*Journal of Business & Economic Statistics*29, 579-586. - Kwan, W., Li, W.K. and Li, G. (2011) On the threshold hyperbolic GARCH models,
*Statistics and Its Interface*4, 159-166. - Li, G. and Li, W.K. (2009) Least absolute deviation estimation for unit root processes with GARCH errors,
*Econometric Theory*25, 1208-1227. - Li, G. and Li, W.K. (2008) Testing for threshold moving average with conditional heteroscedasticity,
*Statistica Sinica*18, 647-665. - Li, G. and Li, W.K. (2008) Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity,
*Biometrika*95, 399-414. - Wang, H., Li, G. and Jiang, G. (2007) Robust regression shrinkage and consistent variable selection via the LAD-LASSO,
*Journal of Business & Economic Statistics*25, 347-355. - Wang, H., Li, G. and 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. - Li, G. and Li, W.K. (2005) Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach,
*Biometrika*92, 691-701. -
Pan, J., Li, G. and Xie, Z. (2002) Stationary solution and parametric estimation for bilinear model driven by ARCH noises,

*Science in China, Series A*45, 1523-1537.## Machine Learning

- 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%) - 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%) - Zhao, J., Fang, Y. and 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. (The acceptance rate is 26%.) - Tu, W., Liu, P., Liu, Y., Kong, L., Li, G., Jiang, B., Yao, H., and 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. - Zhao, J., Huang, F., Lv, J., Duan, Y., Qin, Z., Li, G. and 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%.) - Wang, D., Huang, F., Zhao, J., Li, G. and 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%) - Liu, P., Tu, W., Zhao, J., Liu, Y., Kong, L., Li, G., Jiang, B., Tian, G., and 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%). -
Tu, W., Yang, D., Kong, L., Che, M., Shi, Q., Li, G. and 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%)## Application in Geography

- 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. and 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. - Pei, Q., Li, G., Winterhalder, B.P. and Lowman, M. (2020) Regional patterns of pastoralist migrations under the push of reduced precipitation in imperial China, Global Ecology and Biogeography 29, 433-443.
- Pei, Q., Nowak, Z., Li, G., Xu, C., and 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) - Pei, Q., Zhang, D.D., Li, G., Foret, P. and Lee, H.F. (2016) Temperature and precipitation effects on agrarian economy in late imperial China,
*Environmental Research Letters*11, 064008. - Pei Q., Zhang D.D., Lee F. and 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. - Pei, Q., Zhang, D.D., Li, G. and Lee, H.F. (2015) Climate change and the macroeconomic structure in pre-industrial Europe: new evidence from wavelet analysis,
*PLoS ONE*10(6), e0126480. - Pei, Q., Zhang, D.D., Li, G., Winterhalder, B. and 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. - Pei, Q., Zhang, D.D., Lee, H.F. and Li, G. (2014) Climate change and macro-economic cycles in pre-industrial Europe,
*PLoS ONE*9(2), e88155. -
Pei, Q., Zhang, D.D., Li, G. and 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.## Book Chapter and Invited Discussions

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