Papers by topics

Machine learning theory

“Economic Forecasts Using Many Noises,” with Yuan Liao, Xinjie Ma, and Andreas Neuhierl, 2023
Zhentao Shi, Liangjun Su and Tian Xie (2023): “L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis,” Review of Economics and Statistics.
Zhentao Shi and #Jingyi Huang (2023): “Forward-Selected Panel Data Approach for Program Evaluation,” Journal of Econometrics, 234, 512-535.
Liangjun Su, Zhentao Shi and Peter Phillips (2016): “Identifying Latent Structures in Panel Data,” Econometrica, 84(6), 2215-2264.
“Estimating Large Covariance Matrices with Unobserved Clustering,” with Wenxin Huang, Yiru Wang and Baoning Zheng, 2024.

Jinyuan Chang, Zhentao Shi and Jia Zhang (2022): “Culling the Herd of Moments with Penalized Empirical Likelihood,” Journal of Business & Economic Statistics, 41, 791-805.
Zhentao Shi (2016): “Econometric Estimation with High-Dimensional Moment Equalities,” Journal of Econometrics, 195, 104-119.
Zhentao Shi (2016): “Estimation of Sparse Structural Parameters with Many Endogenous Variables,” Econometric Reviews, 35(8-10): 1582-1608.

“On LASSO for High Dimensional Predictive Regression”, with #Ziwei Mei, 2022.
Ji Hyung Lee, Zhentao Shi, and #Zhan Gao (2022): “On LASSO for Predictive Regression,” Journal of Econometrics, 229(2), 322-340.
Peter Phillips and Zhentao Shi (2021): “Boosting: Why You Can Use the HP Filter,” International Economic Review, 62(2), 521-570.
“The Boosted HP Filter Is More General Than You Might Think,” with #Ziwei Mei and Peter C.B. Phillips, 2022.

#Zhan Gao and Zhentao Shi (2021): “Implementing Convex Optimization in R: Two Econometric Examples,” Computational Economics, 58, 1127-1135.

General econometric theory

“Nickell Meets Stambaugh: A Tale of Two Biases,” with #Chengwang Liao and #Ziwei Mei, 2024.
“Nickell Bias in Panel Local Projection,” with #Ziwei Mei and Liugang Sheng, 2024.
Cheng Hsiao, Zhentao Shi and Qiankun Zhou (2022): “Transformed Estimation for Panel Interactive Effects Models,” Journal of Business & Economic Statistics, 40(4), 1831-1848.
Stephen L. Ross and Zhentao Shi (2022): “Measuring Social Interaction Effects When Instruments Are Weak,” Journal of Business & Economic Statistics, 40(3), 995-1006.
Zhentao Shi and Huanhuan Zheng (2018): “Structural Estimation of Behavioral Heterogeneity,” Journal of Applied Econometrics, 33(5), 690-707.
“A Structural Network Pairwise Regression Model with Unobservable Heterogeneity,” with Xi Chen and #Yapeng Zheng, 2023.
James Chu, Liping Lu and Zhentao Shi (2009): “Pitfalls in Market Timing Test,” Economic Letters, 103(3), 123–126.

“Unveiling Dynamics in Climate Disaster Information Dissemination: A Variance Decomposition Network Approach,” with Sebastien Box-Couillard, Yichun Fan, Yi Huang, Yilan Xu, and Siqi Zheng, 2024
“Team Persistent or Team Transitory? Sectoral Linkage and Inflation Persistence,” with Liugang Sheng and Steve Pak Yeung Wu, 2024
“Forecast Combination in Cryptocurrency Realized Volatility”, with Yue Qiu, Yifan Wang and Tian Xie, 2022.
“Asset Prices and China’s Inflation Forecast: Based on Individual Stock Return and Machine Learning Algorithm,” with Naijing Huang, Yuqing Qi and Jie Liu, 2022
Wei Lin, Zhentao Shi, #Yishu Wang and #Ting Hin Yan (2023): “Unfolding Beijing in a Hedonic Way,” Computational Economics, 61, 317–340.
Ka Yan Cheng, Naijing Huang and Zhentao Shi (2021): “Survay-Based Forecasting: To Average or Not To Average,” in Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka (eds.), Studies in Computational Intelligence: Behavioral Predictive Modeling in Economics, vol. 897, pp 87-104, Springer-Verlag.

Yuefei Zhang, Zhentao Shi, and Yaogguang Chen (2006): [in Chinese] “A Comparative Research on the Efficiency of China Mainland and Hong Kong Stock Markets,” Journal of Financial Research (金融研究), 6, 33-40.
Liuyong Yang and Zhentao Shi (2004): [in Chinese] “An Analysis of Long-run Gold Price Determinants,” Journal of Statistical Research (统计研究), 6 (2), 21-24.