Papers by years

”#” marks supervised postgraduate students

Working papers

• “The Boosted HP Filter Is More General Than You Might Think,” with #Ziwei Mei and Peter C.B. Phillips, 2022.

• “On LASSO for High Dimensional Predictive Regression,” with #Ziwei Mei, 2022.

• “Hidden Nickell Bias in Panel Local Projection,” with #Ziwei Mei and Liugang Sheng, 2023.

• “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

• “A Structural Network Pairwise Regression Model with Unobservable Heterogeneity,” with Xi Chen, 2016.

Publications

• Zhentao Shi, Liangjun Su and Tian Xie (2023): “L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis,” Review of Economics and Statistics, [arXiv:2010.09477] [code].

• Jinyuan Chang, Zhentao Shi and Jia Zhang (2023): “Culling the Herd of Moments with Penalized Empirical Likelihood,” Journal of Business & Economic Statistics, [arXiv:2108.03382].

• Zhentao Shi and #Jingyi Huang (2023): “Forward-Selected Panel Data Approach for Program Evaluation,” Journal of Econometrics [arXiv: 1908.05894] [ package] [supplement].

• Wei Lin, Zhentao Shi, #Yishu Wang and #Ting Hin Yan (2023): “Unfolding Beijing in a Hedonic Way,” Computational Economics, 61, 317–340. [code]

• Ji Hyung Lee, Zhentao Shi, and #Zhan Gao (2022): “On LASSO for Predictive Regression,” Journal of Econometrics, 229(2), 322-340. [arXiv: 1810.03140] [slides] [code]

• 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.

• Peter C.B. Phillips and Zhentao Shi (2021): “Boosting: Why You Can Use the HP Filter,” International Economic Review, 62(2), 521-570. [arXiv: 1905.00175] [slides] [MATLAB, Python and Julia code] [Shiny App] [ package] [vignette] [中文]

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

• Zhentao Shi and Huanhuan Zheng (2018): “Structural Estimation of Behavioral Heterogeneity,” Journal of Applied Econometrics, 33(5), 690-707. [arXiv:1802.03735] [code]

• Liangjun Su, Zhentao Shi and Peter C.B. Phillips (2016): “Identifying Latent Structures in Panel Data,” Econometrica, 84(6), 2215-2264. [supplement] [ package] [code]

• 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.

• James Chu, Liping Lu and Zhentao Shi (2009): “Pitfalls in Market Timing Test,” Economic Letters, 103(3), 123–126.

• [In Chinese] Zhentao Shi, Yuefei Zhang, and Yaogguang Chen (2006): “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.

• [In Chinese] Zhentao Shi and Liuyong Yang (2004): “An Analysis of Long-run Gold Price Determinants,” Journal of Statistical Research (统计研究), 6 (2), 21-24.

Non-peer-reviewed

• #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.