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Ji Hyung Lee

Personal Details

First Name:Ji Hyung
Middle Name:
Last Name:Lee
Suffix:
RePEc Short-ID:ple807
[This author has chosen not to make the email address public]
http://sites.google.com/site/jihyung412/home
Terminal Degree:2013 Economics Department; Yale University (from RePEc Genealogy)

Affiliation

Department of Economics
University of Illinois at Urbana-Champaign

Urbana-Champaign, Illinois (United States)
http://www.economics.illinois.edu/
RePEc:edi:deuiuus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2022. "Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data," Papers 2204.05480, arXiv.org, revised Sep 2022.
  2. Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2021. "Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400," Papers 2105.10007, arXiv.org, revised Sep 2022.
  3. Rui Fan & Ji Hyung Lee & Youngki Shin, 2021. "Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: ALQR Approach," Papers 2101.11568, arXiv.org, revised Aug 2022.
  4. Ji Hyung Lee & Youngki Shin, 2020. "Complete Subset Averaging for Quantile Regressions," Department of Economics Working Papers 2020-03, McMaster University.
  5. Ji Hyung Lee & Oliver Linton & YOON-JAE WHANG, 2018. "Quantilograms under Strong Dependence," Working Paper Series no111, Institute of Economic Research, Seoul National University.
  6. Ji Hyung Lee & Zhentao Shi & Zhan Gao, 2018. "On LASSO for Predictive Regression," Papers 1810.03140, arXiv.org, revised Feb 2021.
  7. Lee, JiHyung, 2015. "Predictive quantile regression with persistent covariates: IVX-QR approach," MPRA Paper 65150, University Library of Munich, Germany.
  8. Peter C.B. Phillips & Ji Hyung Lee, 2012. "VARs with Mixed Roots Near Unity," Cowles Foundation Discussion Papers 1845, Cowles Foundation for Research in Economics, Yale University.

Articles

  1. Lee, Ji Hyung & Linton, Oliver & Whang, Yoon-Jae, 2020. "Quantilograms Under Strong Dependence," Econometric Theory, Cambridge University Press, vol. 36(3), pages 457-487, June.
  2. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.
  3. Lee, Ji Hyung, 2019. "Martingale decomposition and approximations for nonlinearly dependent processes," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 35-42.
  4. Lee, Ji Hyung & Liao, Zhipeng, 2018. "On Standard Inference For Gmm With Local Identification Failure Of Known Forms," Econometric Theory, Cambridge University Press, vol. 34(4), pages 790-814, August.
  5. Lee, Ji Hyung & Phillips, Peter C.B., 2016. "Asset pricing with financial bubble risk," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 590-622.
  6. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
  7. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
  8. Peter C. B. Phillips & Ji Hyung Lee, 2015. "Limit Theory for VARs with Mixed Roots Near Unity," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1035-1056, December.
  9. Phillips, Peter C.B. & Lee, Ji Hyung, 2013. "Predictive regression under various degrees of persistence and robust long-horizon regression," Journal of Econometrics, Elsevier, vol. 177(2), pages 250-264.

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. Ji Hyung Lee & Youngki Shin, 2020. "Complete Subset Averaging for Quantile Regressions," Department of Economics Working Papers 2020-03, McMaster University.

    Cited by:

    1. Victoria Stack & Lana L. Narine, 2022. "Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus," Sustainability, MDPI, vol. 14(2), pages 1-14, January.
    2. Islam, M.S. & Das, Barun K. & Das, Pronob & Rahaman, Md Habibur, 2021. "Techno-economic optimization of a zero emission energy system for a coastal community in Newfoundland, Canada," Energy, Elsevier, vol. 220(C).

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

    Cited by:

    1. 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.
    2. Etienne Wijler, 2022. "A restricted eigenvalue condition for unit-root non-stationary data," Papers 2208.12990, arXiv.org.
    3. 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.
    4. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    5. 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.

  3. Lee, JiHyung, 2015. "Predictive quantile regression with persistent covariates: IVX-QR approach," MPRA Paper 65150, University Library of Munich, Germany.

    Cited by:

    1. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    2. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    3. Sy, Oumar & Zaman, Ashraf Al, 2020. "Is the presidential premium spurious?," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 94-104.
    4. Christou Christina & Naraidoo Ruthira & Gupta Rangan, 2020. "Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-17, June.
    5. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    6. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    7. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    8. Yanbo Liu & Peter C.B. Phillips, 2021. "Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions," Cowles Foundation Discussion Papers 2305, Cowles Foundation for Research in Economics, Yale University.
    9. Christis Katsouris, 2022. "Asymptotic Theory for Moderate Deviations from the Unit Boundary in Quantile Autoregressive Time Series," Papers 2204.02073, arXiv.org.
    10. Ji Hyung Lee & Youngki Shin, 2020. "Complete Subset Averaging for Quantile Regressions," Papers 2003.03299, arXiv.org, revised Jul 2021.
    11. Lee, Ji Hyung & Linton, Oliver & Whang, Yoon-Jae, 2020. "Quantilograms Under Strong Dependence," Econometric Theory, Cambridge University Press, vol. 36(3), pages 457-487, June.
    12. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2017. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Working Papers 201738, University of Pretoria, Department of Economics.
    13. 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.
    14. Jin Seo Cho & Tae-Hwan Kim & Yongcheol Shin, 2014. "Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework," Working papers 2014rwp-69, Yonsei University, Yonsei Economics Research Institute.
    15. Yan, Cheng & Wang, Xichen, 2018. "The non-persistent relationship between foreign equity flows and emerging stock market returns across quantiles," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 38-54.
    16. Xu, Ke-Li, 2021. "On the serial correlation in multi-horizon predictive quantile regression," Economics Letters, Elsevier, vol. 200(C).
    17. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    18. Gungor, Sermin & Luger, Richard, 2020. "Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 750-770.
    19. Demetrescu, Matei & Rodrigues, Paulo MM & Taylor, AM Robert, 2022. "Transformed Regression-based Long-Horizon Predictability Tests," Essex Finance Centre Working Papers 30620, University of Essex, Essex Business School.
    20. Rui Fan & Ji Hyung Lee & Youngki Shin, 2021. "Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: ALQR Approach," Papers 2101.11568, arXiv.org, revised Aug 2022.
    21. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    22. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
    23. 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.
    24. Peter C. B. Phillips, 2015. "Halbert White Jr. Memorial JFEC Lecture: Pitfalls and Possibilities in Predictive Regression†," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(3), pages 521-555.
    25. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.
    26. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.

Articles

  1. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.

    Cited by:

    1. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    2. Grobys, Klaus & Junttila, Juha, 2021. "Speculation and lottery-like demand in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    3. Ji Hyung Lee & Youngki Shin, 2020. "Complete Subset Averaging for Quantile Regressions," Papers 2003.03299, arXiv.org, revised Jul 2021.
    4. Lee, Ji Hyung & Linton, Oliver & Whang, Yoon-Jae, 2020. "Quantilograms Under Strong Dependence," Econometric Theory, Cambridge University Press, vol. 36(3), pages 457-487, June.
    5. 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.
    6. Rui Fan & Ji Hyung Lee & Youngki Shin, 2021. "Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: ALQR Approach," Papers 2101.11568, arXiv.org, revised Aug 2022.
    7. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
    9. 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.
    10. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  2. Lee, Ji Hyung & Liao, Zhipeng, 2018. "On Standard Inference For Gmm With Local Identification Failure Of Known Forms," Econometric Theory, Cambridge University Press, vol. 34(4), pages 790-814, August.

    Cited by:

    1. Yu Zhu, 2020. "Inference in nonparametric/semiparametric moment equality models with shape restrictions," Quantitative Economics, Econometric Society, vol. 11(2), pages 609-636, May.
    2. Ketz, Philipp, 2019. "On asymptotic size distortions in the random coefficients logit model," Journal of Econometrics, Elsevier, vol. 212(2), pages 413-432.
    3. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics Working Papers 2019-04, University of Adelaide, School of Economics.
    4. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    5. Don S. Poskitt, 2020. "On GMM Inference: Partial Identification, Identification Strength, and Non-Standard," Monash Econometrics and Business Statistics Working Papers 40/20, Monash University, Department of Econometrics and Business Statistics.

  3. Lee, Ji Hyung & Phillips, Peter C.B., 2016. "Asset pricing with financial bubble risk," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 590-622.

    Cited by:

    1. Jose Eduardo Gomez-Gonzalez & Sebastian Sanin-Restrepo, 2017. "The Maple Bubble: A History of Migration among Canadian Provinces," Borradores de Economia 992, Banco de la Republica de Colombia.
    2. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    3. Lajos Horv'ath & Zhenya Liu & Shanglin Lu, 2020. "Sequential Monitoring of Changes in Housing Prices," Papers 2002.04101, arXiv.org.
    4. Christian Gouriéroux & Joann Jasiak & Alain Monfort, 2016. "Stationary Bubble Equilibria in Rational Expectation Models," Working Papers 2016-31, Center for Research in Economics and Statistics.
    5. Su, Chi-Wei & Li, Zheng-Zheng & Chang, Hsu-Ling & Lobonţ, Oana-Ramona, 2017. "When Will Occur the Crude Oil Bubbles?," Energy Policy, Elsevier, vol. 102(C), pages 1-6.
    6. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "Sequential monitoring for changes from stationarity to mild non-stationarity," Journal of Econometrics, Elsevier, vol. 215(1), pages 209-238.
    7. Ripamonti, Alexandre & Silva, Diego & Moreira Neto, Eurico, 2018. "Asset Pricing and Asymmetric Information," MPRA Paper 87403, University Library of Munich, Germany.
    8. Daan Steenkamp, 2017. "How bubbly is the New Zealand dollar?," Reserve Bank of New Zealand Discussion Paper Series DP2017/03, Reserve Bank of New Zealand.
    9. Shuping Shi & Peter C. B. Phillips, 2022. "Econometric Analysis of Asset Price Bubbles," Cowles Foundation Discussion Papers 2331, Cowles Foundation for Research in Economics, Yale University.
    10. Andras Fulop & Jun Yu, 2014. "Bayesian Analysis of Bubbles in Asset Prices," Working Papers 04-2014, Singapore Management University, School of Economics.
    11. Park, Alex & Lappas, Petros, 2017. "Evaluating demand charge reduction for commercial-scale solar PV coupled with battery storage," Renewable Energy, Elsevier, vol. 108(C), pages 523-532.
    12. Lennart Ante & Philipp Sandner & Ingo Fiedler, 2018. "Blockchain-Based ICOs: Pure Hype or the Dawn of a New Era of Startup Financing?," JRFM, MDPI, vol. 11(4), pages 1-19, November.
    13. Oladosu, Gbadebo, 2022. "Bubbles in US gasoline prices: Assessing the role of hurricanes and anti–price gouging laws," Journal of Commodity Markets, Elsevier, vol. 27(C).
    14. Moreira, Afonso M. & Martins, Luis F., 2020. "A new mechanism for anticipating price exuberance," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 199-221.
    15. Petr Makovský, 2016. "The relationship between the real economy and financial sector regarding technological bubbles," Ekonomika a Management, Prague University of Economics and Business, vol. 2016(3).
    16. Michael Nwogugu, 2020. "Regret Theory And Asset Pricing Anomalies In Incomplete Markets With Dynamic Un-Aggregated Preferences," Papers 2005.01709, arXiv.org.
    17. Hurn, Stan & Shi, Shuping & Wang, Ben, 2022. "Housing networks and driving forces," Journal of Banking & Finance, Elsevier, vol. 134(C).

  4. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.

    Cited by:

    1. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    2. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    3. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2020. "New robust inference for predictive regressions," Papers 2006.01191, arXiv.org, revised Oct 2021.
    4. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    5. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    6. Stauskas, Ovidijus, 2019. "On the Limit Theory of Mixed to Unity VARs: Panel Setting With Weakly Dependent Errors," Working Papers 2019:2, Lund University, Department of Economics.
    7. Yan, Cheng & Wang, Xichen, 2018. "The non-persistent relationship between foreign equity flows and emerging stock market returns across quantiles," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 38-54.
    8. Shuping Shi & Peter C.B. Phillips, 2020. "Diagnosing Housing Fever with an Econometric Thermometer," Cowles Foundation Discussion Papers 2248, Cowles Foundation for Research in Economics, Yale University.
    9. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    10. Torben G. Andersen & Rasmus T. Varneskov, 2018. "Consistent Inference for Predictive Regressions in Persistent VAR Economies," CREATES Research Papers 2018-09, Department of Economics and Business Economics, Aarhus University.
    11. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
    12. J. Roderick McCrorie, 2021. "Moments in Pearson's Four-Step Uniform Random Walk Problem and Other Applications of Very Well-Poised Generalized Hypergeometric Series," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 244-281, November.
    13. Ovidijus Stauskas, 2020. "On the limit theory of mixed to unity VARs: Panel setting with weakly dependent errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 892-898, November.
    14. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.

  5. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    See citations under working paper version above.
  6. Peter C. B. Phillips & Ji Hyung Lee, 2015. "Limit Theory for VARs with Mixed Roots Near Unity," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1035-1056, December.

    Cited by:

    1. Yubo Tao & Jun Yu, 2017. "Model Selection for Explosive Models," Papers 1703.02720, arXiv.org.
    2. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    3. Stauskas, Ovidijus, 2019. "On the Limit Theory of Mixed to Unity VARs: Panel Setting With Weakly Dependent Errors," Working Papers 2019:2, Lund University, Department of Economics.
    4. Ovidijus Stauskas, 2020. "On the limit theory of mixed to unity VARs: Panel setting with weakly dependent errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 892-898, November.

  7. Phillips, Peter C.B. & Lee, Ji Hyung, 2013. "Predictive regression under various degrees of persistence and robust long-horizon regression," Journal of Econometrics, Elsevier, vol. 177(2), pages 250-264.

    Cited by:

    1. Cui, Liyuan & Hong, Yongmiao & Li, Yingxing, 2021. "Solving Euler equations via two-stage nonparametric penalized splines," Journal of Econometrics, Elsevier, vol. 222(2), pages 1024-1056.
    2. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    3. James A. Duffy & Jerome R. Simons, 2020. "The Cointegrated VAR without Unit Roots: Representation Theory and Asymptotics," Papers 2002.08092, arXiv.org.
    4. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    5. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2020. "New robust inference for predictive regressions," Papers 2006.01191, arXiv.org, revised Oct 2021.
    7. Pavlidis, Efthymios G. & Vasilopoulos, Kostas, 2020. "Speculative bubbles in segmented markets: Evidence from Chinese cross-listed stocks," Journal of International Money and Finance, Elsevier, vol. 109(C).
    8. Daniela Osterrieder & Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés, 2015. "Unbalanced Regressions and the Predictive Equation," CREATES Research Papers 2015-09, Department of Economics and Business Economics, Aarhus University.
    9. Zhenxi Chen & Stefan Reitz, 2020. "Dynamics of the European sovereign bonds and the identification of crisis periods," Empirical Economics, Springer, vol. 58(6), pages 2761-2781, June.
    10. Yong Bao & Aman Ullah, 2021. "Analytical Finite Sample Econometrics: From A. L. Nagar to Now," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 17-37, December.
    11. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    12. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    13. Bingduo Yang & Xiaohui Liu & Liang Peng & Zongwu Cai, 2018. "Unified Tests for a Dynamic Predictive Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201808, University of Kansas, Department of Economics, revised Sep 2018.
    14. Maynard, Alex & Ren, Dongmeng, 2019. "The finite sample power of long-horizon predictive tests in models with financial bubbles," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 418-430.
    15. Aman Ullah & Yong Bao & Ru Zhang, 2014. "Moment Approximation for Unit Root Models with Nonnormal Errors," Working Papers 201401, University of California at Riverside, Department of Economics.
    16. Kaveh Salehzadeh Nobari, 2021. "Pair copula constructions of point-optimal sign-based tests for predictive linear and nonlinear regressions," Papers 2111.04919, arXiv.org.
    17. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    18. Yanbo Liu & Peter C.B. Phillips, 2021. "Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions," Cowles Foundation Discussion Papers 2305, Cowles Foundation for Research in Economics, Yale University.
    19. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    20. Andreou, Elena & Kasparis, Ioannis & Phillips, Peter C. B., 2013. "Nonparametric Predictive Regression," CEPR Discussion Papers 9570, C.E.P.R. Discussion Papers.
    21. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    22. Demetrescu, Matei & Rodrigues, Paulo MM & Taylor, AM Robert, 2022. "Transformed Regression-based Long-Horizon Predictability Tests," Essex Finance Centre Working Papers 30620, University of Essex, Essex Business School.
    23. Torben G. Andersen & Rasmus T. Varneskov, 2018. "Consistent Inference for Predictive Regressions in Persistent VAR Economies," CREATES Research Papers 2018-09, Department of Economics and Business Economics, Aarhus University.
    24. Paulo M.M. Rodrigues & Matei Demetrescu & Iliyan Georgiev & A. M. Robert Taylor, 2019. "Testing for Episodic Predictability in Stock Returns," Working Papers w201906, Banco de Portugal, Economics and Research Department.
    25. Jiti Gao & Bin Peng & Yayi Yan, 2021. "Parameter Stability Testing for Multivariate Dynamic Time-Varying Models," Monash Econometrics and Business Statistics Working Papers 11/21, Monash University, Department of Econometrics and Business Statistics.
    26. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    27. Jeong, Minsoo, 2022. "Modelling persistent stationary processes in continuous time," Economic Modelling, Elsevier, vol. 109(C).
    28. Liu, Guannan & Yao, Shuang, 2020. "A robust test for predictability with unknown persistence," Economics Letters, Elsevier, vol. 189(C).
    29. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    30. Cai, Zongwu & Juhl, Ted & Yang, Bingduo, 2015. "Functional index coefficient models with variable selection," Journal of Econometrics, Elsevier, vol. 189(2), pages 272-284.
    31. Efthymios G. Pavlidis & Ivan Paya & David A. Peel, 2018. "Using Market Expectations to Test for Speculative Bubbles in the Crude Oil Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(5), pages 833-856, August.
    32. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
    33. J. Roderick McCrorie, 2021. "Moments in Pearson's Four-Step Uniform Random Walk Problem and Other Applications of Very Well-Poised Generalized Hypergeometric Series," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 244-281, November.
    34. Liu, Xiaohui & Yang, Bingduo & Cai, Zongwu & Peng, Liang, 2019. "A unified test for predictability of asset returns regardless of properties of predicting variables," Journal of Econometrics, Elsevier, vol. 208(1), pages 141-159.
    35. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
    36. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.
    37. Demetrescu, Matei, 2014. "Enhancing the local power of IVX-based tests in predictive regressions," Economics Letters, Elsevier, vol. 124(2), pages 269-273.
    38. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.
    39. Yayi Yan & Jiti Gao & Bin Peng, 2021. "Asymptotics for Time-Varying Vector MA(∞) Processes," Monash Econometrics and Business Statistics Working Papers 22/21, Monash University, Department of Econometrics and Business Statistics.

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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 8 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 (6) 2012-01-18 2015-06-27 2018-11-05 2020-03-16 2021-02-22 2022-05-16. Author is listed
  2. NEP-ETS: Econometric Time Series (3) 2012-01-18 2018-11-05 2021-02-22
  3. NEP-ORE: Operations Research (3) 2019-11-25 2020-03-16 2021-02-22
  4. NEP-FOR: Forecasting (1) 2015-06-27
  5. NEP-PBE: Public Economics (1) 2021-05-31
  6. NEP-PUB: Public Finance (1) 2021-05-31

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