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Zhentao Shi

Personal Details

First Name:Zhentao
Middle Name:
Last Name:Shi
Suffix:
RePEc Short-ID:psh1166
[This author has chosen not to make the email address public]

Affiliation

School of Economics
Georgia Institute of Technology

Atlanta, Georgia (United States)
http://www.econ.gatech.edu/
RePEc:edi:segatus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org.
  2. Jinyuan Chang & Zhentao Shi & Jia Zhang, 2021. "Culling the herd of moments with penalized empirical likelihood," Papers 2108.03382, arXiv.org, revised May 2022.
  3. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
  4. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
  5. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers 2212, Cowles Foundation for Research in Economics, Yale University.
  6. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
  7. Zhentao Shi & Huanhuan Zheng, 2018. "Structural Estimation of Behavioral Heterogeneity," Papers 1802.03735, arXiv.org, revised Jun 2018.
  8. Ji Hyung Lee & Zhentao Shi & Zhan Gao, 2018. "On LASSO for Predictive Regression," Papers 1810.03140, arXiv.org, revised Feb 2021.
  9. Zhan Gao & Zhentao Shi, 2018. "Implementing Convex Optimization in R: Two Econometric Examples," Papers 1806.10423, arXiv.org, revised Aug 2019.
  10. Stephen L. Ross & Zhentao Shi, 2016. "Measuring Social Interaction Effects when Instruments are Weak," Working papers 2016-37, University of Connecticut, Department of Economics.
  11. Liangjun Su & Zhentao Shi & Peter C.B. Phillips, 2014. "Identifying Latent Structures in Panel Data," Cowles Foundation Discussion Papers 1965, Cowles Foundation for Research in Economics, Yale University.

Articles

  1. Stephen L. Ross & Zhentao Shi, 2022. "Measuring Social Interaction Effects When Instruments Are Weak," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 995-1006, June.
  2. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
  3. Cheng Hsiao & Zhentao Shi & Qiankun Zhou, 2022. "Transformed Estimation for Panel Interactive Effects Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1831-1848, October.
  4. Zhan Gao & Zhentao Shi, 2021. "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1127-1135, December.
  5. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
  6. Zhentao Shi & Huanhuan Zheng, 2018. "Structural estimation of behavioral heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 690-707, August.
  7. Zhentao Shi, 2016. "Estimation of Sparse Structural Parameters with Many Endogenous Variables," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1582-1608, December.
  8. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
  9. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
  10. Chu, Chia-Shang & Lu, Liping & Shi, Zhentao, 2009. "Pitfalls in market timing test," Economics Letters, Elsevier, vol. 103(3), pages 123-126, June.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org.

    Cited by:

    1. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org.
    2. Eva Biswas & Farzad Sabzikar & Peter C. B. Phillips, 2022. "Boosting the HP Filter for Trending Time Series with Long Range Dependence," Cowles Foundation Discussion Papers 2347, Cowles Foundation for Research in Economics, Yale University.

  2. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    2. Peter C. B. Phillips & Xiaohu Wang & Yonghui Zhang, 2019. "HAR Testing for Spurious Regression in Trend," Econometrics, MDPI, vol. 7(4), pages 1-28, December.
    3. Alain Hecq & Elisa Voisin, 2019. "Predicting crashes in oil prices during the COVID-19 pandemic with mixed causal-noncausal models," Papers 1911.10916, arXiv.org, revised May 2022.
    4. Baffes,John,Kabundi,Alain Ntumba, 2021. "Commodity Price Shocks : Order within Chaos ?," Policy Research Working Paper Series 9792, The World Bank.

  3. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers 2212, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    2. Neslihan Sakarya & Robert M. de Jong, 2022. "The spectral analysis of the Hodrick–Prescott filter," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 479-489, May.
    3. Sokbae (Simon) Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2020. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," CeMMAP working papers CWP32/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Hall, Viv B & Thomson, Peter, 2020. "Does Hamilton’s OLS regression provide a “better alternative†to the Hodrick-Prescott filter? A New Zealand Business Cycle Perspective," Working Paper Series 8956, Victoria University of Wellington, School of Economics and Finance.
    5. Jacobo, Juan, 2022. "A multi time-scale theory of economic growth and cycles," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 143-155.
    6. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    7. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    8. Melina Dritsaki & Chaido Dritsaki, 2022. "Comparison of HP Filter and the Hamilton’s Regression," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    9. Dąbrowski, Marek A. & Widiantoro, Dimas Mukhlas, 2022. "Effectiveness and conduct of macroprudential policy in Indonesia in 2003-2020: Evidence from the structural VAR models," MPRA Paper 112963, University Library of Munich, Germany.
    10. Giuliano Queiroz Ferreira & Leonardo Bornacki Mattos, 2022. "Regime-dependent price puzzle in the Brazilian economy: evidence from VAR and FAVAR approaches," SN Business & Economics, Springer, vol. 2(9), pages 1-28, September.
    11. Hiroshi Yamada & Ruoyi Bao, 2022. "$$\ell _{1}$$ ℓ 1 Common Trend Filtering," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1005-1025, March.
    12. Mariarosaria Comunale & Dmitrij Celov, 2021. "Business cycles in the EU: A comprehensive comparison across methods," Bank of Lithuania Discussion Paper Series 26, Bank of Lithuania.
    13. Tamás Sebestyén & Zita Iloskics, 2020. "Do economic shocks spread randomly?: A topological study of the global contagion network," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    14. Mateusz Tomal, 2022. "Testing for overall and cluster convergence of housing rents using robust methodology: evidence from Polish provincial capitals," Empirical Economics, Springer, vol. 62(4), pages 2023-2055, April.

  4. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.

    Cited by:

    1. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org.

  5. Zhentao Shi & Huanhuan Zheng, 2018. "Structural Estimation of Behavioral Heterogeneity," Papers 1802.03735, arXiv.org, revised Jun 2018.

    Cited by:

    1. Zheng, Huanhuan, 2020. "Coordinated bubbles and crashes," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    2. Huanhuan Zheng & Haiqiang Chen, 2019. "Price informativeness and adaptive trading," Journal of Evolutionary Economics, Springer, vol. 29(4), pages 1315-1342, September.

  6. Ji Hyung Lee & Zhentao Shi & Zhan Gao, 2018. "On LASSO for Predictive Regression," Papers 1810.03140, arXiv.org, revised Feb 2021.

    Cited by:

    1. Chaohua Dong & Jiti Gao & Yundong Tu & Bin Peng, 2023. "Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models," Papers 2301.06631, arXiv.org.
    2. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Monash Econometrics and Business Statistics Working Papers 18/21, Monash University, Department of Econometrics and Business Statistics.
    3. Julien Hambuckers & Li Sun & Luca Trapin, 2023. "Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors," Papers 2301.01362, arXiv.org.
    4. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2021. "Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice," Papers 2111.02023, arXiv.org.
    5. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org.
    6. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    7. Etienne Wijler, 2022. "A restricted eigenvalue condition for unit-root non-stationary data," Papers 2208.12990, arXiv.org.
    8. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.

  7. Liangjun Su & Zhentao Shi & Peter C.B. Phillips, 2014. "Identifying Latent Structures in Panel Data," Cowles Foundation Discussion Papers 1965, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Ruiqi Liu & Ben Boukai & Zuofeng Shang, 2019. "Statistical Inference on Partially Linear Panel Model under Unobserved Linearity," Papers 1911.08830, arXiv.org.
    2. Andrea Orame, 2020. "The role of bank supply in the Italian credit market: evidence from a new regional survey," Temi di discussione (Economic working papers) 1279, Bank of Italy, Economic Research and International Relations Area.
    3. Juan Romero-Padilla, 2018. "A method for clustering panel data based on parameter homogeneity," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(3), pages 1-3.
    4. Mammen, Enno & Wilke, Ralf A. & Zapp, Kristina Maria, 2022. "Estimation of group structures in panel models with individual fixed effects," ZEW Discussion Papers 22-023, ZEW - Leibniz Centre for European Economic Research.
    5. Nibbering, D. & Paap, R., 2019. "Panel Forecasting with Asymmetric Grouping," Econometric Institute Research Papers EI-2019-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    7. Max Cytrynbaum, 2020. "Blocked Clusterwise Regression," Papers 2001.11130, arXiv.org.
    8. Fernández-Val, Iván & Gao, Wayne Yuan & Liao, Yuan & Vella, Francis, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," IZA Discussion Papers 15236, Institute of Labor Economics (IZA).
    9. Myungkou Shin, 2022. "Finitely Heterogeneous Treatment Effect in Event-study," Papers 2204.02346, arXiv.org, revised Dec 2022.
    10. Friedel Bolle & Jonathan H. W. Tan, 2021. "Behavioral types of the dark side: identifying heterogeneous conflict strategies," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 7(1), pages 49-63, September.
    11. Srinivasan, Shweta & Kholod, Nazar & Chaturvedi, Vaibhav & Ghosh, Probal Pratap & Mathur, Ritu & Clarke, Leon & Evans, Meredydd & Hejazi, Mohamad & Kanudia, Amit & Koti, Poonam Nagar & Liu, Bo & Parik, 2018. "Water for electricity in India: A multi-model study of future challenges and linkages to climate change mitigation," Applied Energy, Elsevier, vol. 210(C), pages 673-684.
    12. Levent Kutlu & Robin C. Sickles & Mike G. Tsionas & Emmanuel Mamatzakis, 2022. "Heterogeneous decision-making and market power: an application to Eurozone banks," Empirical Economics, Springer, vol. 63(6), pages 3061-3092, December.
    13. Huang, Danyang & Hu, Wei & Jing, Bingyi & Zhang, Bo, 2023. "Grouped spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    14. Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2019. "Parametric Inference on the Mean of Functional Data Applied to Lifetime Income Curves," Working papers 2019rwp-153, Yonsei University, Yonsei Economics Research Institute.
    15. Ayden Higgins & Federico Martellosio, 2019. "Shrinkage Estimation of Network Spillovers with Factor Structured Errors," Papers 1909.02823, arXiv.org, revised Nov 2021.
    16. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    17. Sophie-Charlotte Klose, 2020. "Identifying Latent Structures in Maternal Employment: Evidence on the German Parental Benefit Reform," Papers 2011.03541, arXiv.org.
    18. Yoonseok Lee & Donggyu Sul, 2021. "Trimmed Mean Group Estimation," Center for Policy Research Working Papers 237, Center for Policy Research, Maxwell School, Syracuse University.
    19. Krasnokutskaya, Elena & Song, Kyungchul & Tang, Xun, 2022. "Estimating unobserved individual heterogeneity using pairwise comparisons," Journal of Econometrics, Elsevier, vol. 226(2), pages 477-497.
    20. Adrian Bruhin & Kelly Janizzi & Christian Thöni, 2019. "Uncovering the Heterogeneity behind Cross-Cultural Variation in Antisocial Punishment," Cahiers de Recherches Economiques du Département d'économie 19.08, Université de Lausanne, Faculté des HEC, Département d’économie.
    21. Li, Kunpeng & Cui, Guowei & Lu, Lina, 2020. "Efficient estimation of heterogeneous coefficients in panel data models with common shocks," Journal of Econometrics, Elsevier, vol. 216(2), pages 327-353.
    22. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    23. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    24. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
    25. Liu, Ruiqi & Shang, Zuofeng & Zhang, Yonghui & Zhou, Qiankun, 2020. "Identification and estimation in panel models with overspecified number of groups," Journal of Econometrics, Elsevier, vol. 215(2), pages 574-590.
    26. Feng, Guohua & Gao, Jiti & Peng, Bin & Zhang, Xiaohui, 2017. "A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks," Journal of Econometrics, Elsevier, vol. 196(1), pages 68-82.
    27. Wang, Wuyi & Phillips, Peter C.B. & Su, Liangjun, 2018. "The Heterogeneous Effects of the Minimum Wage on Employment Across States," Economics and Statistics Working Papers 11-2018, Singapore Management University, School of Economics.
    28. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    29. Simon Freyaldenhoven & Christian Hansen & Jorge Perez Perez & Jesse Shapiro, 2021. "Visualization, Identification, and stimation in the Linear Panel Event-Study Design," Working Papers 21-44, Federal Reserve Bank of Philadelphia.
    30. Yannick V. Markhof, 2020. "Divide to Conquer? Latent Preference Types and Country-level Heterogeneity," CSAE Working Paper Series 2020-05, Centre for the Study of African Economies, University of Oxford.
    31. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    32. Ziwei Mei & Zhentao Shi & Peter C. B. Phillips, 2022. "The boosted HP filter is more general than you might think," Cowles Foundation Discussion Papers 2348, Cowles Foundation for Research in Economics, Yale University.
    33. Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
    34. Okui, Ryo & Wang, Wendun, 2021. "Heterogeneous structural breaks in panel data models," Journal of Econometrics, Elsevier, vol. 220(2), pages 447-473.
    35. Carlos Lamarche & Thomas Parker, 2020. "Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data," Papers 2004.05127, arXiv.org, revised May 2022.
    36. Ryo Okui & Takahide Yanagi, 2014. "Panel Data Analysis with Heterogeneous Dynamics," KIER Working Papers 906, Kyoto University, Institute of Economic Research.
    37. Jiangtao Duan & Wei Gao & Hao Qu & Hon Keung Tony, 2019. "Subspace Clustering for Panel Data with Interactive Effects," Papers 1909.09928, arXiv.org, revised Feb 2021.
    38. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    39. Dzemski, Andreas & Okui, Ryo, 2021. "Convergence rate of estimators of clustered panel models with misclassification," Economics Letters, Elsevier, vol. 203(C).
    40. Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Cowles Foundation Discussion Papers 2323, Cowles Foundation for Research in Economics, Yale University.
    41. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    42. Levent Kutlu & Kien C. Tran & Mike G. Tsionas, 2020. "Unknown latent structure and inefficiency in panel stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 54(1), pages 75-86, August.
    43. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    44. Miao, Ke & Li, Kunpeng & Su, Liangjun, 2020. "Panel threshold models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 219(1), pages 137-170.
    45. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Wang, Wuyi & Su, Liangjun, 2021. "Identifying latent group structures in nonlinear panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 272-295.
    47. Zhan Gao & M. Hashem Pesaran, 2022. "Identification and Estimation of Categorical Random Coefficient Models," CESifo Working Paper Series 9714, CESifo.
    48. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.
    49. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org.
    50. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org.
    51. Harold D. Chiang & Joel Rodrigue & Yuya Sasaki, 2019. "Post-Selection Inference in Three-Dimensional Panel Data," Papers 1904.00211, arXiv.org, revised Apr 2019.
    52. Nicolas Apfel & Helmut Farbmacher & Rebecca Groh & Martin Huber & Henrika Langen, 2022. "Detecting Grouped Local Average Treatment Effects and Selecting True Instruments," Papers 2207.04481, arXiv.org.
    53. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    54. Chang Cai & Sandy Dall’Erba, 2021. "On the evaluation of heterogeneous climate change impacts on US agriculture: does group membership matter?," Climatic Change, Springer, vol. 167(1), pages 1-23, July.
    55. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach," SEEDS Working Papers 0521, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised May 2021.
    56. Fei Liu & Jiti Gao & Yanrong Yang, 2019. "Nonparametric Estimation in Panel Data Models with Heterogeneity and Time Varyingness," Monash Econometrics and Business Statistics Working Papers 24/19, Monash University, Department of Econometrics and Business Statistics.
    57. Francesco Fallucchi & Andrea Mercatanti & Jan Niederreiter, 2021. "Identifying types in contest experiments," International Journal of Game Theory, Springer;Game Theory Society, vol. 50(1), pages 39-61, March.
    58. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    59. Su, Liangjun & Ju, Gaosheng, 2018. "Identifying latent grouped patterns in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 554-573.
    60. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org.
    61. Huang, Wenxin & Jin, Sainan & Phillips, Peter C.B. & Su, Liangjun, 2021. "Nonstationary panel models with latent group structures and cross-section dependence," Journal of Econometrics, Elsevier, vol. 221(1), pages 198-222.
    62. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    63. Wei Chen & Xilu Chen & Chang-Tai Hsieh & Zheng Song, 2019. "A Forensic Examination of China's National Accounts," NBER Working Papers 25754, National Bureau of Economic Research, Inc.
    64. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    65. Dong Hwan Oh & Andrew J. Patton, 2021. "Dynamic Factor Copula Models with Estimated Cluster Assignments," Finance and Economics Discussion Series 2021-029r1, Board of Governors of the Federal Reserve System (U.S.), revised 06 May 2022.
    66. Jianning Kong & Peter C.B. Phillips & Donggyu Sul, 2017. "Weak s- Convergence: Theory and Applications," Cowles Foundation Discussion Papers 2072, Cowles Foundation for Research in Economics, Yale University.
    67. Hie Joo Ahn & Yun Liu & Yeonwoo Rho, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Finance and Economics Discussion Series 2020-082, Board of Governors of the Federal Reserve System (U.S.).
    68. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    69. Jia Chen, 2018. "Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models," Discussion Papers 18/15, Department of Economics, University of York.
    70. Zhan Gao & Zhentao Shi, 2021. "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1127-1135, December.
    71. Michael Vogt & Oliver Linton, 2017. "Classification of non-parametric regression functions in longitudinal data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 5-27, January.
    72. Tadao Hoshino, 2020. "A Pairwise Strategic Network Formation Model with Group Heterogeneity: With an Application to International Travel," Papers 2012.14886, arXiv.org, revised Feb 2021.
    73. Shujie Ma & Liangjun Su & Yichong Zhang, 2020. "Detecting Latent Communities in Network Formation Models," Papers 2005.03226, arXiv.org, revised Mar 2021.
    74. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    75. Denis Chetverikov & Elena Manresa, 2022. "Spectral and post-spectral estimators for grouped panel data models," Papers 2212.13324, arXiv.org, revised Dec 2022.
    76. Jiti Gao & Kai Xia & Huanjun Zhu, 2019. "Heterogeneous Panel Data Models with Cross-Sectional Dependence," Working Papers 2019-07-09, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    77. Hafner, Christian & Walders, Fabian, 2017. "Heterogeneous Liquidity Effects in Corporate Bond Spreads," LIDAM Reprints ISBA 2017037, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    78. Arturas Juodis & Yiannis Karavias, 2019. "Partially heterogeneous tests for Granger non-causality in panel data," Bank of Lithuania Working Paper Series 59, Bank of Lithuania.
    79. Wagner, Martin & Grabarczyk, Peter & Hong, Seung Hyun, 2020. "Fully modified OLS estimation and inference for seemingly unrelated cointegrating polynomial regressions and the environmental Kuznets curve for carbon dioxide emissions," Journal of Econometrics, Elsevier, vol. 214(1), pages 216-255.
    80. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    81. Gao, Jiti & Xia, Kai & Zhu, Huanjun, 2020. "Heterogeneous panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 219(2), pages 329-353.
    82. Yu Hao & Hiroyuki Kasahara, 2022. "Testing the Number of Components in Finite Mixture Normal Regression Model with Panel Data," Papers 2210.02824, arXiv.org.
    83. Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
    84. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    85. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.

Articles

  1. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    See citations under working paper version above.
  2. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    See citations under working paper version above.
  3. Zhentao Shi & Huanhuan Zheng, 2018. "Structural estimation of behavioral heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 690-707, August.
    See citations under working paper version above.
  4. Zhentao Shi, 2016. "Estimation of Sparse Structural Parameters with Many Endogenous Variables," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1582-1608, December.

    Cited by:

    1. Ando, Tomohiro & Sueishi, Naoya, 2019. "Regularization parameter selection for penalized empirical likelihood estimator," Economics Letters, Elsevier, vol. 178(C), pages 1-4.
    2. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    3. Tomohiro Ando & Naoya Sueishi, 2019. "On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator," Econometrics, MDPI, vol. 7(1), pages 1-14, March.

  5. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.

    Cited by:

    1. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    2. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    3. Hao, Bowen & Prokhorov, Artem & Qian, Hailong, 2019. "Moment Redundancy Test with Application to Efficiency-Improving Copulas," Working Papers BAWP-2019-05, University of Sydney Business School, Discipline of Business Analytics.
    4. Ziwei Mei & Zhentao Shi & Peter C. B. Phillips, 2022. "The boosted HP filter is more general than you might think," Cowles Foundation Discussion Papers 2348, Cowles Foundation for Research in Economics, Yale University.
    5. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    6. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org.
    7. Zhan Gao & Zhentao Shi, 2021. "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1127-1135, December.
    8. Tomohiro Ando & Naoya Sueishi, 2019. "On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator," Econometrics, MDPI, vol. 7(1), pages 1-14, March.
    9. Tang, Niansheng & Yan, Xiaodong & Zhao, Puying, 2018. "Exponentially tilted likelihood inference on growing dimensional unconditional moment models," Journal of Econometrics, Elsevier, vol. 202(1), pages 57-74.

  6. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
    See citations under working paper version above.
  7. Chu, Chia-Shang & Lu, Liping & Shi, Zhentao, 2009. "Pitfalls in market timing test," Economics Letters, Elsevier, vol. 103(3), pages 123-126, June.

    Cited by:

    1. Tsuchiya, Yoichi, 2016. "Directional analysis of fiscal sustainability: Revisiting Domar's debt sustainability condition," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 189-201.
    2. Chou, Cheng & Chu, Chia-Shang J., 2011. "Market timing: Recent development and a new test," Economics Letters, Elsevier, vol. 111(2), pages 105-109, May.
    3. Tsuchiya, Yoichi, 2013. "Do corporate executives have accurate predictions for the economy? A directional analysis," Economic Modelling, Elsevier, vol. 30(C), pages 167-174.
    4. Young Bin Ahn & Yoichi Tsuchiya, 2016. "Directional analysis of consumers’ forecasts of inflation in a small open economy: evidence from South Korea," Applied Economics, Taylor & Francis Journals, vol. 48(10), pages 854-864, February.
    5. Tsuchiya, Yoichi, 2016. "Do production managers predict turning points? A directional analysis," Economic Modelling, Elsevier, vol. 58(C), pages 1-8.
    6. Tsuchiya, Yoichi, 2013. "Are government and IMF forecasts useful? An application of a new market-timing test," Economics Letters, Elsevier, vol. 118(1), pages 118-120.
    7. Y. Tsuchiya, 2014. "A directional evaluation of corporate executives' exchange rate forecasts," Applied Economics, Taylor & Francis Journals, vol. 46(1), pages 95-101, January.
    8. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    9. Y. Tsuchiya, 2014. "Are consumer sentiments useful in Japan? An application of a new market-timing test," Applied Economics Letters, Taylor & Francis Journals, vol. 21(5), pages 356-359, March.
    10. Tsuchiya, Yoichi, 2014. "Purchasing and supply managers provide early clues on the direction of the US economy: An application of a new market-timing test," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 599-618.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 13 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (8) 2014-08-25 2018-07-16 2018-11-05 2019-05-06 2019-08-26 2020-11-09 2021-08-23 2022-10-17. Author is listed
  2. NEP-BIG: Big Data (6) 2018-07-16 2019-05-06 2019-08-26 2019-09-16 2019-12-16 2022-10-17. Author is listed
  3. NEP-ETS: Econometric Time Series (5) 2018-11-05 2019-05-06 2019-09-16 2021-08-23 2022-10-17. Author is listed
  4. NEP-CMP: Computational Economics (3) 2019-09-16 2019-12-16 2022-10-17
  5. NEP-EDU: Education (2) 2016-12-18 2016-12-18
  6. NEP-ORE: Operations Research (2) 2019-12-16 2021-08-23
  7. NEP-SEA: South East Asia (2) 2014-08-25 2015-01-09
  8. NEP-FOR: Forecasting (1) 2020-11-09
  9. NEP-ISF: Islamic Finance (1) 2021-08-23
  10. NEP-LAB: Labour Economics (1) 2016-12-18
  11. NEP-NET: Network Economics (1) 2016-12-18
  12. NEP-SOC: Social Norms & Social Capital (1) 2016-12-18
  13. NEP-URE: Urban & Real Estate Economics (1) 2016-12-18

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