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Zhentao Shi

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, revised Apr 2024.

    Cited by:

    1. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
    3. Ye Lu & Adrian Pagan, 2023. "To Boost or Not to Boost? That is the Question," Working Papers 2023-05, University of Sydney, School of Economics.
    4. 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. 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.

    Cited by:

    1. Nabil Bouamara & S'ebastien Laurent & Shuping Shi, 2023. "Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications," Papers 2303.13406, arXiv.org, revised Jun 2023.

  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. Alban Moura, 2022. "Why you should never use the Hodrick-Prescott Filter: Comment," BCL working papers 162, Central Bank of Luxembourg.
    2. Kady Keita & Camelia Turcu, 2022. "Promoting Counter-Cyclical Fiscal Policy: Fiscal Rules Versus Institutions," Post-Print hal-04059017, HAL.
    3. Zhang, Zuomin & Dai, Ling, 2023. "The bank loan distribution effect of government spending expansion: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 89(C).
    4. Kazeem O. Isah & Abdulkader C. Mahomedy & Elias A. Udeaja & Ojo J. Adelakun & Yusuf Yakubu & Danmecca Musa, 2022. "Revisiting the accuracy of inflation forecasts in Nigeria: The oil price–exchange rate–asymmetry perspectives," South African Journal of Economics, Economic Society of South Africa, vol. 90(3), pages 329-348, September.
    5. 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.
    6. Baffes, John & Kabundi, Alain, 2024. "Do supercycles dominate commodity price movements?," Economics Letters, Elsevier, vol. 237(C).
    7. 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.
    8. Claudio Morana, 2021. "A new macro-financial condition index for the euro area," Working Paper series 21-07, Rimini Centre for Economic Analysis, revised Sep 2021.
    9. Ye Lu & Adrian Pagan, 2023. "To Boost or Not to Boost? That is the Question," Working Papers 2023-05, University of Sydney, School of Economics.
    10. 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.
    11. Baffes, John & Kabundi, Alain, 2023. "Commodity price shocks: Order within chaos?," Resources Policy, Elsevier, vol. 83(C).
    12. Hiroshi Yamada, 2023. "Quantile regression version of Hodrick–Prescott filter," Empirical Economics, Springer, vol. 64(4), pages 1631-1645, April.
    13. 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.
    14. Xu, Can, 2023. "Do households react to policy uncertainty by increasing savings?," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 770-785.
    15. 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.
    16. Canhong Wen & Xueqin Wang & Aijun Zhang, 2023. "ℓ 0 Trend Filtering," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1491-1510, November.
    17. Amor, Thouraya Hadj & Nouira, Ridha & Rault, Christophe & Sova, Anamaria Diana, 2023. "Real exchange rate misalignments and economic growth in Tunisia: New evidence from a threshold analysis of asymmetric adjustments," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 215-227.
    18. 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.
    19. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    20. 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 21184, Victoria University of Wellington, School of Economics and Finance.
    21. 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 21070, Victoria University of Wellington, School of Economics and Finance.
    22. Carlos D. Ramirez, 2024. "The effect of economic policy uncertainty under fractional integration," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(1), pages 89-110, January.
    23. Richard K. Crump & Nikolay Gospodinov & Hunter Wieman, 2023. "Sparse Trend Estimation," Staff Reports 1049, Federal Reserve Bank of New York.
    24. 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.
    25. 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.
    26. Haitham A. Al-Zoubi, 2024. "An affine model for short rates when monetary policy is path dependent," Review of Derivatives Research, Springer, vol. 27(2), pages 151-201, July.
    27. 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.
    28. Wei Lin & Zhentao Shi & Yishu Wang & Ting Hin Yan, 2023. "Unfolding Beijing in a Hedonic Way," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 317-340, January.
    29. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    30. Melina Dritsaki & Chaido Dritsaki, 2022. "Comparison of HP Filter and the Hamilton’s Regression," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    31. Shi, Zhentao & Huang, Jingyi, 2023. "Forward-selected panel data approach for program evaluation," Journal of Econometrics, Elsevier, vol. 234(2), pages 512-535.
    32. Raj Rajesh & Deba Prasad Rath, 2023. "House price convergence: evidence from India," Asia-Pacific Journal of Regional Science, Springer, vol. 7(3), pages 721-747, September.
    33. Yves Schueler, 2024. "Filtering economic time series: On the cyclical properties of Hamilton’s regression filter and the Hodrick-Prescott filter," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 54, October.
    34. Andrian, Leandro Gaston & Valencia, Oscar & Hirs, Jorge & Urrea Rios, Ivan Leonardo, 2022. "Fiscal Rules and Economic Cycles: Quality (Always) Matters," IDB Publications (Working Papers) 12639, Inter-American Development Bank.
    35. Zihan Jin & Hiroshi Yamada, 2024. "Boosted Whittaker–Henderson Graduation," Mathematics, MDPI, vol. 12(21), pages 1-18, October.
    36. Lynda Sanderson, 2024. "Born in bad times: Economic conditions, selection and employment," Working Papers 2024/01, New Zealand Productivity Commission.

  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. Hongjun Li & Zheng Li & Cheng Hsiao, 2023. "Assessing the impacts of pandemic and the increase in minimum down payment rate on Shanghai housing prices," Empirical Economics, Springer, vol. 64(6), pages 2661-2682, June.
    2. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    3. Luya Wang & Zheng Li & Qi Li, 2023. "A Tale of Two Policies: Examining Treatment Effects on Housing Prices in Shenzhen, China," Annals of Economics and Finance, Society for AEF, vol. 24(2), pages 277-288, November.
    4. Wei Lin & Zhentao Shi & Yishu Wang & Ting Hin Yan, 2023. "Unfolding Beijing in a Hedonic Way," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 317-340, January.

  5. 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. Alain Hecq & Elisa Voisin, 2023. "Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 209-233, Emerald Group Publishing Limited.
    3. 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.
    4. Baffes, John & Kabundi, Alain, 2023. "Commodity price shocks: Order within chaos?," Resources Policy, Elsevier, vol. 83(C).
    5. Amor, Thouraya Hadj & Nouira, Ridha & Rault, Christophe & Sova, Anamaria Diana, 2023. "Real exchange rate misalignments and economic growth in Tunisia: New evidence from a threshold analysis of asymmetric adjustments," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 215-227.
    6. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.

  6. 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.

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

    Cited by:

    1. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    2. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    3. 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.
    4. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models," Monash Econometrics and Business Statistics Working Papers 2/23, Monash University, Department of Econometrics and Business Statistics.
    5. Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023. "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Etienne Wijler, 2022. "A restricted eigenvalue condition for unit-root non-stationary data," Papers 2208.12990, arXiv.org.
    7. Zhou, Weilun & Gao, Jiti & Harris, David & Kew, Hsein, 2024. "Semi-parametric single-index predictive regression models with cointegrated regressors," Journal of Econometrics, Elsevier, vol. 238(1).
    8. 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.
    9. Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    10. 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.
    11. Campeanu Emilia Mioara & Boitan Iustina Alina & Anghel Dan Gabriel, 2023. "Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach," Management & Marketing, Sciendo, vol. 18(s1), pages 315-339, December.
    12. 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.
    13. David Neto, 2023. "Penalized leads-and-lags cointegrating regression: a simulation study and two empirical applications," Empirical Economics, Springer, vol. 65(2), pages 949-971, August.

  8. 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. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    2. 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, revised Oct 2023.
    3. Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
    4. Ruiqi Liu & Ben Boukai & Zuofeng Shang, 2019. "Statistical Inference on Partially Linear Panel Model under Unobserved Linearity," Papers 1911.08830, arXiv.org.
    5. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    6. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    7. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    8. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers 06/15, Institute for Fiscal Studies.
    9. 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.
    10. Yoonseok Lee & Donggyu Sul, 2022. "Trimmed Mean Group Estimation," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 177-202, Emerald Group Publishing Limited.
    11. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    12. 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.
    13. 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.
    14. 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.
    15. Saptorshee Kanto Chakraborty & Antoine Mandel, 2024. "Understanding EU regional macroeconomic tipping points using panel threshold technique," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-30, June.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Daniel J. Lewis & Davide Melcangi & Laura Pilossoph & Aidan Toner-Rodgers, 2022. "Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression," Staff Reports 1033, Federal Reserve Bank of New York.
    22. 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.
    23. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    24. André Lucas & Julia Schaumburg & Bernd Schwaab, 2020. "Dynamic clustering of multivariate panel data," Tinbergen Institute Discussion Papers 20-009/III, Tinbergen Institute.
    25. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    26. 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.
    27. Max Cytrynbaum, 2020. "Blocked Clusterwise Regression," Papers 2001.11130, arXiv.org.
    28. Sumon Bhaumik & Subhasish M. Chowdhury & Ralitza Dimova & Hanna Fromell, 2023. "Identity, Communication, and Conflict: An Experiment," Economics Working Papers 2023-02, Department of Economics and Business Economics, Aarhus University.
    29. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    30. 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.
    31. Wang, Wei & Xiao, Zhijie & Ren, Yanyan & Yan, Xiaodong, 2023. "A bi-integrative analysis of two-dimensional heterogeneous panel data models," Economics Letters, Elsevier, vol. 230(C).
    32. 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).
    33. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    34. Myungkou Shin, 2022. "Finitely Heterogeneous Treatment Effect in Event-study," Papers 2204.02346, arXiv.org, revised Oct 2024.
    35. Guohua Feng & Jiti Gao & Bin Peng & Xiaohui Zhang, 2015. "A Varying-Coefficient Panel Data Model with Fixed Effects: Theory and an Application to U.S. Commercial Banks," Monash Econometrics and Business Statistics Working Papers 9/15, Monash University, Department of Econometrics and Business Statistics.
    36. 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.
    37. Yu, Lu & Gu, Jiaying & Volgushev, Stanislav, 2024. "Spectral clustering with variance information for group structure estimation in panel data," Journal of Econometrics, Elsevier, vol. 241(1).
    38. 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.
    39. Yiren Wang & Peter C. B. Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Cowles Foundation Discussion Papers 2364, Cowles Foundation for Research in Economics, Yale University.
    40. 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.
    41. Huang, Danyang & Hu, Wei & Jing, Bingyi & Zhang, Bo, 2023. "Grouped spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    42. Jorge A. Rivero, 2023. "Unobserved Grouped Heteroskedasticity and Fixed Effects," Papers 2310.14068, arXiv.org, revised Oct 2023.
    43. Yang, Shuquan & Ling, Nengxiang, 2023. "Robust projected principal component analysis for large-dimensional semiparametric factor modeling," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    44. 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.
    45. 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.
    46. Weijie Cui & Yong Li, 2023. "Bicluster Analysis of Heterogeneous Panel Data via M-Estimation," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    47. Pigini, Claudia & Pionati, Alessandro & Valentini, Francesco, 2023. "Specification testing with grouped fixed effects," MPRA Paper 117821, University Library of Munich, Germany.
    48. In Choi & Rui Lin & Yongcheol Shin, 2020. "Canonical Correlation-based Model Selection for the Multilevel Factors," Working Papers 2008, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    49. 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.
    50. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    51. Ayden Higgins & Federico Martellosio, 2019. "Shrinkage Estimation of Network Spillovers with Factor Structured Errors," Papers 1909.02823, arXiv.org, revised Nov 2021.
    52. Zhao, Jiayang & Liu, Jie, 2023. "Homogeneous analysis on network effects in network autoregressive model," Finance Research Letters, Elsevier, vol. 58(PD).
    53. Dong, Yingjie & Huang, Wenxin & Tse, Yiu-Kuen, 2023. "Price comovement and market segmentation of Chinese A- and H-shares: Evidence from a panel latent-factor model," Journal of International Money and Finance, Elsevier, vol. 131(C).
    54. Mehrabani, Ali, 2023. "Estimation and identification of latent group structures in panel data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1464-1482.
    55. 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.).
    56. Lu, Xun & Su, Liangjun, 2023. "Uniform inference in linear panel data models with two-dimensional heterogeneity," Journal of Econometrics, Elsevier, vol. 235(2), pages 694-719.
    57. 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.
    58. Sophie-Charlotte Klose, 2020. "Identifying Latent Structures in Maternal Employment: Evidence on the German Parental Benefit Reform," Papers 2011.03541, arXiv.org.
    59. Kathleen T. Li, 2024. "Frontiers: A Simple Forward Difference-in-Differences Method," Marketing Science, INFORMS, vol. 43(2), pages 267-279, March.
    60. Jorge Eduardo Pérez, 2022. "Visualization, identification, and estimation in linear panel event-study design," Colombian Stata Users' Group Meetings 2022 05, Stata Users Group.
    61. Li, Donglin & Wang, Wenyue & Ren, Yanyan, 2024. "Quantile estimation of heterogenous panel quantile model with group structure," Economics Letters, Elsevier, vol. 241(C).
    62. 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.
    63. Krasnokutskaya, Elena & Song, Kyungchul & Tang, Xun, 2022. "Estimating unobserved individual heterogeneity using pairwise comparisons," Journal of Econometrics, Elsevier, vol. 226(2), pages 477-497.
    64. Ryo Okui & Wendun Wang, 2018. "Heterogeneous structural breaks in panel data models," Papers 1801.04672, arXiv.org, revised Nov 2018.
    65. Dzemski, Andreas & Okui, Ryo, 2020. "Convergence rate of estimators of clustered panel models with misclassication," Working Papers in Economics 790, University of Gothenburg, Department of Economics.
    66. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
    67. Bruhin, Adrian & Janizzi, Kelly & Thöni, Christian, 2020. "Uncovering the heterogeneity behind cross-cultural variation in antisocial punishment," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 291-308.
    68. Sophie Therese Schneider & Konstantin M. Wacker, 2022. "Explaining the global landscape of foreign direct investment: Knowledge capital, gravity, and the role of culture and institutions," The World Economy, Wiley Blackwell, vol. 45(10), pages 3080-3108, October.
    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. Archer Gong Zhang & Jiahua Chen, 2023. "Optimal Estimation under a Semiparametric Density Ratio Model," Papers 2309.09103, arXiv.org.
    72. 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.
    73. Carlos Lamarche & Thomas Parker, 2022. "Wild Bootstrap Inference For Penalized Quantile Regression For Longitudinal Data," Working Papers 22003 Classification-C15,, University of Waterloo, Department of Economics.
    74. 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.
    75. Thomas Wiemann, 2023. "Optimal Categorical Instrumental Variables," Papers 2311.17021, arXiv.org, revised May 2024.
    76. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    77. 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.
    78. 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.
    79. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    80. 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.
    81. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).
    82. 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.
    83. 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.
    84. Shujie Ma & Liangjun Su & Yichong Zhang, 2020. "Detecting Latent Communities in Network Formation Models," Papers 2005.03226, arXiv.org, revised Mar 2021.
    85. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    86. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    87. Wang, Wuyi & Su, Liangjun, 2017. "Identifying Latent Group Structures in Nonlinear Panels," Economics and Statistics Working Papers 19-2017, Singapore Management University, School of Economics.
    88. 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.
    89. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    90. Denis Chetverikov & Elena Manresa, 2022. "Spectral and post-spectral estimators for grouped panel data models," Papers 2212.13324, arXiv.org, revised Dec 2022.
    91. 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.
    92. Gao, Z. & Pesaran, M. H., 2022. "Identification and Estimation of Categorical Random Coeficient Models," Cambridge Working Papers in Economics 2228, Faculty of Economics, University of Cambridge.
    93. Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
    94. 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).
    95. 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.
    96. Didier Nibbering & Richard Paap, 2024. "Forecasting carbon emissions using asymmetric grouping," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2228-2256, September.
    97. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    98. Likai Chen & Georg Keilbar & Liangjun Su & Weining Wang, 2023. "Inference on many jumps in nonparametric panel regression models," Papers 2312.01162, arXiv.org, revised Aug 2024.
    99. Katerina Chrysikou & George Kapetanios, 2024. "Heterogeneous Grouping Structures in Panel Data," Papers 2407.19509, arXiv.org.
    100. 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.
    101. 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.
    102. 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.
    103. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
    104. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    105. 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.
    106. Chiang, Harold D. & Rodrigue, Joel & Sasaki, Yuya, 2023. "Post-Selection Inference In Three-Dimensional Panel Data," Econometric Theory, Cambridge University Press, vol. 39(3), pages 623-658, June.
    107. Miao, Ke & Li, Kunpeng & Su, Liangjun, 2020. "Panel threshold models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 219(1), pages 137-170.
    108. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    109. Sun, Yan & Wan, Chuang & Zhang, Wenyang & Zhong, Wei, 2024. "A Multi-Kink quantile regression model with common structure for panel data analysis," Journal of Econometrics, Elsevier, vol. 239(2).
    110. Jiti Gao & Kai Xia, 2017. "Heterogeneous panel data models with cross-sectional dependence," Monash Econometrics and Business Statistics Working Papers 16/17, Monash University, Department of Econometrics and Business Statistics.
    111. 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.
    112. 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.
    113. 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.
    114. Yu Hao & Hiroyuki Kasahara, 2022. "Testing the Number of Components in Finite Mixture Normal Regression Model with Panel Data," Papers 2210.02824, arXiv.org, revised Jun 2023.
    115. Hong, Shengjie & Su, Liangjun & Jiang, Tao, 2023. "Profile GMM estimation of panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 927-948.
    116. 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.
    117. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    118. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.
    119. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Estimation of Grouped Time-Varying Network Vector Autoregression Models," Papers 2303.10117, arXiv.org, revised Mar 2024.
    120. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.
    121. Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023. "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, vol. 126(C).
    122. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    123. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    124. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
    125. Langevin, R.;, 2024. "Consistent Estimation of Finite Mixtures: An Application to Latent Group Panel Structures," Health, Econometrics and Data Group (HEDG) Working Papers 24/16, HEDG, c/o Department of Economics, University of York.

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. 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. Victor Chernozhukov & Ivan Fernandez-Val & Chen Huang & Weining Wang, 2024. "Arellano-bond lasso estimator for dynamic linear panel models," CeMMAP working papers 09/24, Institute for Fiscal Studies.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Wei Lin & Zhentao Shi & Yishu Wang & Ting Hin Yan, 2023. "Unfolding Beijing in a Hedonic Way," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 317-340, January.

  5. 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. 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.
    3. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    4. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.

  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, 2013. "Are government and IMF forecasts useful? An application of a new market-timing test," Economics Letters, Elsevier, vol. 118(1), pages 118-120.
    2. 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.
    3. Y. Tsuchiya, 2014. "A directional evaluation of corporate executives' exchange rate forecasts," Applied Economics, Taylor & Francis Journals, vol. 46(1), pages 95-101, January.
    4. 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.
    5. 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.
    6. Tsuchiya, Yoichi, 2013. "Do corporate executives have accurate predictions for the economy? A directional analysis," Economic Modelling, Elsevier, vol. 30(C), pages 167-174.
    7. 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.
    8. Tsuchiya, Yoichi, 2016. "Do production managers predict turning points? A directional analysis," Economic Modelling, Elsevier, vol. 58(C), pages 1-8.
    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.

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