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Xiaofeng Shao

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. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2020. "Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective," Papers 2007.04553, arXiv.org.

    Cited by:

    1. Zhang, Wenjia & Wu, Yulin & Deng, Guobang, 2024. "Social and spatial disparities in individuals’ mobility response time to COVID-19: A big data analysis incorporating changepoint detection and accelerated failure time models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    2. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    3. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    4. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    5. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    6. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
    7. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Working Papers 14/20, Sapienza University of Rome, DISS.
    8. Geon Lee & Se-eun Yoon & Kijung Shin, 2022. "Simple epidemic models with segmentation can be better than complex ones," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-18, January.
    9. Liu, Jingyuan & Sun, Ao & Ke, Yuan, 2024. "A generalized knockoff procedure for FDR control in structural change detection," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    11. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.
    12. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
    13. Ziyuan Xia & Jeffery Chen & Anchen Sun, 2021. "Mining the Relationship Between COVID-19 Sentiment and Market Performance," Papers 2101.02587, arXiv.org, revised Mar 2023.
    14. Rolando de la Cruz & Cristian Meza & Nicolás Narria & Claudio Fuentes, 2022. "A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    15. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
    16. Zhao, Wenbiao & Zhu, Lixing, 2024. "Detecting change structures of nonparametric regressions," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    17. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

  2. Yeonwoo Rho & Xiaofeng Shao, 2018. "Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors," Papers 1802.05333, arXiv.org.

    Cited by:

    1. Chang, Jinyuan & Cheng, Guanghui & Yao, Qiwei, 2022. "Testing for unit roots based on sample autocovariances," LSE Research Online Documents on Economics 114620, London School of Economics and Political Science, LSE Library.
    2. Jinyuan Chang & Guanghui Cheng & Qiwei Yao, 2022. "Testing for unit roots based on sample autocovariances [Heteroskedasticity and autocorrelation consistent covariance matrix estimation]," Biometrika, Biometrika Trust, vol. 109(2), pages 543-550.

Articles

  1. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    See citations under working paper version above.
  2. Yangfan Zhang & Runmin Wang & Xiaofeng Shao, 2022. "Adaptive Inference for Change Points in High-Dimensional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(540), pages 1751-1762, October.

    Cited by:

    1. Jiang, Feiyu & Wang, Runmin & Shao, Xiaofeng, 2023. "Robust inference for change points in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    2. Anders Bredahl Kock & David Preinerstorfer, 2024. "Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $\infty$-norm," Papers 2407.17888, arXiv.org, revised Oct 2024.
    3. Cui, Junfeng & Wang, Guanghui & Zou, Changliang & Wang, Zhaojun, 2023. "Change-point testing for parallel data sets with FDR control," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

  3. Guochang Wang & Ke Zhu & Xiaofeng Shao, 2022. "Testing for the Martingale Difference Hypothesis in Multivariate Time Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 980-994, June.

    Cited by:

    1. Andrea Bucci, 2024. "A sequential test procedure for the choice of the number of regimes in multivariate nonlinear models," Papers 2406.02152, arXiv.org.
    2. De Gooijer, Jan G., 2023. "On portmanteau-type tests for nonlinear multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    3. Kunyang Song & Feiyu Jiang & Ke Zhu, 2024. "Estimation for conditional moment models based on martingale difference divergence," Papers 2404.11092, arXiv.org.
    4. Zhu, Ke, 2023. "A new generalized exponentially weighted moving average quantile model and its statistical inference," Journal of Econometrics, Elsevier, vol. 237(1).

  4. Chung Eun Lee & Xiaofeng Shao, 2020. "Volatility Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Multivariate Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 80-92, January.

    Cited by:

    1. Li, Lu & Ke, Chenlu & Yin, Xiangrong & Yu, Zhou, 2023. "Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Lee, Chung Eun & Zhang, Xin, 2024. "Conditional mean dimension reduction for tensor time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).

  5. C E Lee & X Zhang & X Shao, 2020. "Testing conditional mean independence for functional data," Biometrika, Biometrika Trust, vol. 107(2), pages 331-346.

    Cited by:

    1. Eduardo García‐Portugués & Javier Álvarez‐Liébana & Gonzalo Álvarez‐Pérez & Wenceslao González‐Manteiga, 2021. "A goodness‐of‐fit test for the functional linear model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 502-528, June.
    2. Lai, Tingyu & Zhang, Zhongzhan & Wang, Yafei, 2021. "A kernel-based measure for conditional mean dependence," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).

  6. Rho, Yeonwoo & Shao, Xiaofeng, 2019. "Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors," Econometric Theory, Cambridge University Press, vol. 35(1), pages 142-166, February.
    See citations under working paper version above.
  7. Chung Eun Lee & Xiaofeng Shao, 2018. "Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 216-229, January.

    Cited by:

    1. Liu, Jicai & Xu, Peirong & Lian, Heng, 2019. "Estimation for single-index models via martingale difference divergence," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 271-284.
    2. Shang, Du & Shang, Pengjian, 2022. "The dependence measurements based on martingale difference correlation and distance correlation: Efficient tools to distinguish different complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Lai, Tingyu & Zhang, Zhongzhan & Wang, Yafei, 2021. "A kernel-based measure for conditional mean dependence," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Luca Mattia Rolla & Alessandro Giovannelli, 2022. "The Forecasting performance of the Factor model with Martingale Difference errors," Papers 2205.10256, arXiv.org, revised Jun 2023.
    5. Li, Lu & Ke, Chenlu & Yin, Xiangrong & Yu, Zhou, 2023. "Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    6. Emmanuel Selorm Tsyawo, 2021. "Feasible IV Regression without Excluded Instruments," Papers 2103.09621, arXiv.org, revised Nov 2022.
    7. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    8. Lee, Chung Eun & Zhang, Xin, 2024. "Conditional mean dimension reduction for tensor time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).

  8. Shun Yao & Xianyang Zhang & Xiaofeng Shao, 2018. "Testing mutual independence in high dimension via distance covariance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 455-480, June.

    Cited by:

    1. Tarik Bahraoui & Jean‐François Quessy, 2022. "Tests of multivariate copula exchangeability based on Lévy measures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1215-1243, September.
    2. Laverny, Oskar & Masiello, Esterina & Maume-Deschamps, Véronique & Rullière, Didier, 2021. "Dependence structure estimation using Copula Recursive Trees," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    3. Xu, Kai & Cheng, Qing, 2024. "Test of conditional independence in factor models via Hilbert–Schmidt independence criterion," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    4. Carlos Velasco & Xuexin Wang, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2024-09-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    5. Matsui, Muneya & Mikosch, Thomas & Roozegar, Rasool & Tafakori, Laleh, 2022. "Distance covariance for random fields," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 280-322.
    6. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    7. Ivair R. Silva & Yan Zhuang & Julio C. A. da Silva Junior, 2022. "Kronecker delta method for testing independence between two vectors in high-dimension," Statistical Papers, Springer, vol. 63(2), pages 343-365, April.
    8. Yongshuai Chen & Wenwen Guo & Hengjian Cui, 2024. "On the test of covariance between two high-dimensional random vectors," Statistical Papers, Springer, vol. 65(5), pages 2687-2717, July.
    9. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    10. Beaulieu Guillaume Boglioni & de Micheaux Pierre Lafaye & Ouimet Frédéric, 2021. "Counterexamples to the classical central limit theorem for triplewise independent random variables having a common arbitrary margin," Dependence Modeling, De Gruyter, vol. 9(1), pages 424-438, January.
    11. Jin, Ze & Matteson, David S., 2018. "Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 304-322.
    12. Chu, Ba, 2023. "A distance-based test of independence between two multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 195(C).

  9. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

    Cited by:

    1. Zhang, Xianyang, 2016. "Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework," Journal of Econometrics, Elsevier, vol. 193(1), pages 123-146.

  10. Srijan Sengupta & Stanislav Volgushev & Xiaofeng Shao, 2016. "A Subsampled Double Bootstrap for Massive Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1222-1232, July.

    Cited by:

    1. Dimitris N Politis, 2024. "Scalable subsampling: computation, aggregation and inference," Biometrika, Biometrika Trust, vol. 111(1), pages 347-354.
    2. Xuejun Ma & Shaochen Wang & Wang Zhou, 2022. "Statistical inference in massive datasets by empirical likelihood," Computational Statistics, Springer, vol. 37(3), pages 1143-1164, July.
    3. Guangbao Guo & Yue Sun & Xuejun Jiang, 2020. "A partitioned quasi-likelihood for distributed statistical inference," Computational Statistics, Springer, vol. 35(4), pages 1577-1596, December.
    4. Kaizhao Liu & Jose Blanchet & Lexing Ying & Yiping Lu, 2024. "Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty," Papers 2404.19145, arXiv.org, revised Apr 2024.
    5. Ma, Xuejun & Wang, Shaochen & Zhou, Wang, 2021. "Testing multivariate quantile by empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 182(C).

  11. Yeonwoo Rho & Xiaofeng Shao, 2015. "Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 444-457, July.

    Cited by:

    1. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    2. Michal Pešta & Martin Wendler, 2020. "Nuisance-parameter-free changepoint detection in non-stationary series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 379-408, June.

  12. Xiaofeng Shao, 2015. "Self-Normalization for Time Series: A Review of Recent Developments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1797-1817, December.

    Cited by:

    1. Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
    2. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2021. "Inference in heavy-tailed non-stationary multivariate time series," Papers 2107.13894, arXiv.org.
    3. Jiang, Feiyu & Wang, Runmin & Shao, Xiaofeng, 2023. "Robust inference for change points in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    4. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    5. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    6. Horváth, Lajos & Rice, Gregory, 2019. "Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 138-165.
    7. Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Deep Adaptive Input Normalization for Time Series Forecasting," Papers 1902.07892, arXiv.org, revised Sep 2019.
    8. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Data Normalization for Bilinear Structures in High-Frequency Financial Time-series," Papers 2003.00598, arXiv.org, revised Jul 2020.
    9. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    10. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    11. Xuexin Wang & Yixiao Sun, 2020. "An Asymptotic F Test for Uncorrelatedness in the Presence of Time Series Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 536-550, July.
    12. 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.
    13. Yacouba Boubacar Maïnassara & Youssef Esstafa & Bruno Saussereau, 2021. "Estimating FARIMA models with uncorrelated but non-independent error terms," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 549-608, October.
    14. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
    15. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    16. Kim, Bo Gyeong & Shin, Dong Wan, 2020. "A mean-difference test based on self-normalization for alternating regime index data sets," Economics Letters, Elsevier, vol. 193(C).
    17. Chen, Willa W. & Deo, Rohit S., 2018. "Subsampling based inference for U statistics under thick tails using self-normalization," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 95-103.
    18. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    19. Aigner, Maximilian & Chavez-Demoulin, Valérie & Guillou, Armelle, 2022. "Measuring and comparing risks of different types," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 1-21.
    20. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2020. "Determining the rank of cointegration with infinite variance," Discussion Papers 20/01, University of Nottingham, Granger Centre for Time Series Econometrics.
    21. Karsten Reichold & Carsten Jentsch, 2022. "A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions," Papers 2204.01373, arXiv.org.
    22. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    23. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2021. "Bilinear Input Normalization for Neural Networks in Financial Forecasting," Papers 2109.00983, arXiv.org.

  13. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.

    Cited by:

    1. Sun, Jiajing & Hong, Yongmiao & Linton, Oliver & Zhao, Xiaolu, 2022. "Adjusted-range self-normalized confidence interval construction for censored dependent data," Economics Letters, Elsevier, vol. 220(C).
    2. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    3. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.

  14. Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.

    Cited by:

    1. Liu, Jicai & Xu, Peirong & Lian, Heng, 2019. "Estimation for single-index models via martingale difference divergence," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 271-284.
    2. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
    3. Shang, Du & Shang, Pengjian, 2022. "The dependence measurements based on martingale difference correlation and distance correlation: Efficient tools to distinguish different complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    5. Craig, Sarah J.C. & Kenney, Ana M. & Lin, Junli & Paul, Ian M. & Birch, Leann L. & Savage, Jennifer S. & Marini, Michele E. & Chiaromonte, Francesca & Reimherr, Matthew L. & Makova, Kateryna D., 2023. "Constructing a polygenic risk score for childhood obesity using functional data analysis," Econometrics and Statistics, Elsevier, vol. 25(C), pages 66-86.
    6. Zhong, Wei & Wang, Jiping & Chen, Xiaolin, 2021. "Censored mean variance sure independence screening for ultrahigh dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    7. Lai, Tingyu & Zhang, Zhongzhan & Wang, Yafei, 2021. "A kernel-based measure for conditional mean dependence," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    8. Kunyang Song & Feiyu Jiang & Ke Zhu, 2024. "Estimation for conditional moment models based on martingale difference divergence," Papers 2404.11092, arXiv.org.
    9. Carlos Velasco & Xuexin Wang, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2024-09-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    10. Xu, Kai & Zhou, Yeqing, 2021. "Projection-averaging-based cumulative covariance and its use in goodness-of-fit testing for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    11. Xiaolin Chen & Xiaojing Chen & Yi Liu, 2019. "A note on quantile feature screening via distance correlation," Statistical Papers, Springer, vol. 60(5), pages 1741-1762, October.
    12. Luca Mattia Rolla & Alessandro Giovannelli, 2022. "The Forecasting performance of the Factor model with Martingale Difference errors," Papers 2205.10256, arXiv.org, revised Jun 2023.
    13. Hyokyoung G. Hong & Xuerong Chen & David C. Christiani & Yi Li, 2018. "Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes," Biometrics, The International Biometric Society, vol. 74(2), pages 421-429, June.
    14. Li, Lu & Ke, Chenlu & Yin, Xiangrong & Yu, Zhou, 2023. "Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    15. Yongshuai Chen & Wenwen Guo & Hengjian Cui, 2024. "On the test of covariance between two high-dimensional random vectors," Statistical Papers, Springer, vol. 65(5), pages 2687-2717, July.
    16. Denuit, Michel & Robert, Christian Y., 2023. "From risk reduction to risk elimination by conditional mean risk sharing of independent losses," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 46-59.
    17. Emmanuel Selorm Tsyawo, 2021. "Feasible IV Regression without Excluded Instruments," Papers 2103.09621, arXiv.org, revised Nov 2022.
    18. Congran Yu & Wenwen Guo & Xinyuan Song & Hengjian Cui, 2023. "Feature screening with latent responses," Biometrics, The International Biometric Society, vol. 79(2), pages 878-890, June.
    19. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    20. Liu, Jicai & Si, Yuefeng & Niu, Yong & Zhang, Riquan, 2022. "Projection quantile correlation and its use in high-dimensional grouped variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    21. Zhang, Shucong & Zhou, Yong, 2018. "Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 1-13.
    22. Su, Liangjun & Zheng, Xin, 2017. "A martingale-difference-divergence-based test for specification," Economics Letters, Elsevier, vol. 156(C), pages 162-167.
    23. Emmanuel Selorm Tsyawo & Abdul-Nasah Soale, 2021. "A Distance Covariance-based Estimator," Papers 2102.07008, arXiv.org, revised Sep 2024.
    24. Xi Wu & Shifeng Xiong & Weiyan Mu, 2023. "An Ensemble Method for Feature Screening," Mathematics, MDPI, vol. 11(2), pages 1-14, January.
    25. Emmanuel Jordy Menvouta & Sven Serneels & Tim Verdonck, 2022. "Sparse dimension reduction based on energy and ball statistics," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 951-975, December.
    26. Jin, Ze & Matteson, David S., 2018. "Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 304-322.
    27. Zhu, Ke, 2023. "A new generalized exponentially weighted moving average quantile model and its statistical inference," Journal of Econometrics, Elsevier, vol. 237(1).
    28. Chen, Xiaolin & Chen, Xiaojing & Wang, Hong, 2018. "Robust feature screening for ultra-high dimensional right censored data via distance correlation," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 118-138.
    29. Wang, Pei & Yin, Xiangrong & Yuan, Qingcong & Kryscio, Richard, 2021. "Feature filter for estimating central mean subspace and its sparse solution," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    30. Lee, Chung Eun & Zhang, Xin, 2024. "Conditional mean dimension reduction for tensor time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).

  15. Xianyang Zhang & Bo Li & Xiaofeng Shao, 2014. "Self-normalization for Spatial Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 311-324, June.

    Cited by:

    1. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    2. Mackenbach, Johan P. & McKee, Martin, 2015. "Government, politics and health policy: A quantitative analysis of 30 European countries," Health Policy, Elsevier, vol. 119(10), pages 1298-1308.

  16. Xiaofeng Shao & Dimitris N. Politis, 2013. "Fixed b subsampling and the block bootstrap: improved confidence sets based on p-value calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 161-184, January.

    Cited by:

    1. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Srijan Sengupta & Xiaofeng Shao & Yingchuan Wang, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 315-326, May.

  17. Zhou Zhou & Xiaofeng Shao, 2013. "Inference for linear models with dependent errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 323-343, March.

    Cited by:

    1. Eguren-Martin, Fernando & O'Neill, Cian & Sokol, Andrej & von dem Berge, Lukas, 2024. "Capital flows-at-risk: Push, pull and the role of policy," Journal of International Money and Finance, Elsevier, vol. 147(C).
    2. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    3. Porshnev, Alexander V. & Lakshina, Valeriya V. & Redkin, Ilya E., 2016. "Using Emotional Markers' Frequencies in Stock Market ARMAX-GARCH Model," MPRA Paper 82875, University Library of Munich, Germany.
    4. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    5. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    6. Fernando Eguren-Martin & Andrej Sokol, 2022. "Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(3), pages 487-519, September.
    7. Feiyu Jiang & Dong Li & Ke Zhu, 2019. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Papers 1907.04147, arXiv.org, revised Oct 2020.
    8. Yamada, Hiroshi & Yoon, Gawon, 2014. "When Grilli and Yang meet Prebisch and Singer: Piecewise linear trends in primary commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 193-207.
    9. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.
    10. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    11. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    12. Jiang, Feiyu & Li, Dong & Zhu, Ke, 2021. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 224(2), pages 306-329.
    13. Alexander Porshnev & Valeria Lakshina & Ilya Redkin, 2016. "Could Emotional Markers in Twitter Posts Add Information to the Stock Market Armax-Garch Model," HSE Working papers WP BRP 54/FE/2016, National Research University Higher School of Economics.

  18. Xiaofeng Shao, 2012. "Parametric Inference in Stationary Time Series Models with Dependent Errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 772-783, December.

    Cited by:

    1. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    2. Yacouba Boubacar Maïnassara & Youssef Esstafa & Bruno Saussereau, 2021. "Estimating FARIMA models with uncorrelated but non-independent error terms," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 549-608, October.

  19. Xiaofeng Shao, 2011. "A simple test of changes in mean in the possible presence of long‐range dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 598-606, November.

    Cited by:

    1. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "Persistence, non-linearities and structural breaks in European stock market indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 50-61.
    2. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    3. Annika Betken, 2016. "Testing for Change-Points in Long-Range Dependent Time Series by Means of a Self-Normalized Wilcoxon Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 785-809, November.
    4. Wingert, Simon & Mboya, Mwasi Paza & Sibbertsen, Philipp, 2020. "Distinguishing between breaks in the mean and breaks in persistence under long memory," Economics Letters, Elsevier, vol. 193(C).
    5. Daiqing Xi & Tianxiao Pang, 2021. "Estimating multiple breaks in mean sequentially with fractionally integrated errors," Statistical Papers, Springer, vol. 62(1), pages 451-494, February.
    6. Wenger, Kai & Leschinski, Christian, 2018. "Fixed-Bandwidth CUSUM Tests Under Long Memory," Hannover Economic Papers (HEP) dp-647, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    8. Dolado, Juan J & Rachinger, Heiko & Velasco, Carlos, 2020. "LM tests for joint breaks in the dynamics and level of a long-memory time series," CEPR Discussion Papers 15435, C.E.P.R. Discussion Papers.
    9. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    10. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2018. "A simple test on structural change in long-memory time series," Economics Letters, Elsevier, vol. 163(C), pages 90-94.
    11. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    12. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    13. Seong Yeon Chang & Pierre Perron, 2014. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Boston University - Department of Economics - Working Papers Series wp2015-011, Boston University - Department of Economics, revised 20 Sep 2015.
    14. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    15. Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    16. Michal Pešta & Martin Wendler, 2020. "Nuisance-parameter-free changepoint detection in non-stationary series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 379-408, June.
    17. Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    18. Sibbertsen, Philipp & Wenger, Kai & Wingert, Simon, 2020. "Testing for Multiple Structural Breaks in Multivariate Long Memory Time Series," Hannover Economic Papers (HEP) dp-676, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    19. Mustafa R. K{i}l{i}nc{c} & Michael Massmann, 2024. "The modified conditional sum-of-squares estimator for fractionally integrated models," Papers 2404.12882, arXiv.org.
    20. Aeneas Rooch & Ieva Zelo & Roland Fried, 2019. "Estimation methods for the LRD parameter under a change in the mean," Statistical Papers, Springer, vol. 60(1), pages 313-347, February.
    21. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    22. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).

  20. Shao, Xiaofeng, 2011. "Testing For White Noise Under Unknown Dependence And Its Applications To Diagnostic Checking For Time Series Models," Econometric Theory, Cambridge University Press, vol. 27(2), pages 312-343, April.

    Cited by:

    1. Mengya Liu & Fukan Zhu & Ke Zhu, 2020. "Multi-frequency-band tests for white noise under heteroskedasticity," Papers 2004.09161, arXiv.org.
    2. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    3. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    4. Li, Linyuan & Yao, Shan & Duchesne, Pierre, 2014. "On wavelet-based testing for serial correlation of unknown form using Fan’s adaptive Neyman method," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 308-327.
    5. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    6. Xiao, Han & Wu, Wei Biao, 2019. "Portmanteau Test and Simultaneous Inference for Serial Covariances," IRTG 1792 Discussion Papers 2019-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Xuexin Wang & Yixiao Sun, 2020. "An Asymptotic F Test for Uncorrelatedness in the Presence of Time Series Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 536-550, July.
    8. Yacouba Boubacar Maïnassara & Youssef Esstafa & Bruno Saussereau, 2021. "Estimating FARIMA models with uncorrelated but non-independent error terms," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 549-608, October.
    9. Jinyuan Chang & Qing Jiang & Xiaofeng Shao, 2022. "Testing the martingale difference hypothesis in high dimension," Papers 2209.04770, arXiv.org, revised Sep 2022.
    10. Guay, Alain & Guerre, Emmanuel & Lazarová, Štěpána, 2013. "Robust adaptive rate-optimal testing for the white noise hypothesis," Journal of Econometrics, Elsevier, vol. 176(2), pages 134-145.
    11. Chen, Min & Zhu, Ke, 2014. "Sign-based specification tests for martingale difference with conditional heteroscedasity," MPRA Paper 56347, University Library of Munich, Germany.
    12. Characiejus, Vaidotas & Rice, Gregory, 2020. "A general white noise test based on kernel lag-window estimates of the spectral density operator," Econometrics and Statistics, Elsevier, vol. 13(C), pages 175-196.
    13. Liudas Giraitis & Yufei Li & Peter C.B. Phillips, 2023. "Robust Inference on Correlation under General Heterogeneity," Cowles Foundation Discussion Papers 2354, Cowles Foundation for Research in Economics, Yale University.
    14. Colin M. Gallagher & Thomas J. Fisher, 2015. "On Weighted Portmanteau Tests For Time-Series Goodness-Of-Fit," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 67-83, January.
    15. Zhu, Ke, 2012. "A mixed portmanteau test for ARMA-GARCH model by the quasi-maximum exponential likelihood estimation approach," MPRA Paper 40382, University Library of Munich, Germany.
    16. Lee, Chung Eun & Zhang, Xin, 2024. "Conditional mean dimension reduction for tensor time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    17. Chang, Jinyuan & Yao, Qiwei & Zhou, Wen, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," LSE Research Online Documents on Economics 68531, London School of Economics and Political Science, LSE Library.

  21. Shao, Xiaofeng, 2011. "A bootstrap-assisted spectral test of white noise under unknown dependence," Journal of Econometrics, Elsevier, vol. 162(2), pages 213-224, June.

    Cited by:

    1. Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
    2. Mengya Liu & Fukan Zhu & Ke Zhu, 2020. "Multi-frequency-band tests for white noise under heteroskedasticity," Papers 2004.09161, arXiv.org.
    3. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    4. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    5. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2014. "Inference in VARs with Conditional Heteroskedasticity of Unknown Form," Working Papers 14-21, University of Mannheim, Department of Economics.
    6. Li, Linyuan & Duchesne, Pierre & Liou, Chu Pheuil, 2021. "On diagnostic checking in ARMA models with conditionally heteroscedastic martingale difference using wavelet methods," Econometrics and Statistics, Elsevier, vol. 19(C), pages 169-187.
    7. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    8. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.
    9. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.
    10. Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
    11. Doukhan, Paul & Lang, Gabriel & Leucht, Anne & Neumann, Michael H., 2014. "Dependent wild bootstrap for the empirical process," Working Papers 35246, University of Mannheim, Department of Economics.
    12. Skrobotov Anton & Cavaliere Giuseppe & Taylor Robert, 2016. "Wild Bootstrap Seasonal Unit Root Tests for Time Series with Periodic Non-Stationary Volatility," Working Papers wpaper-2016-269, Gaidar Institute for Economic Policy, revised 2016.
    13. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.
    14. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    15. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
    16. Kaiji Motegi & Xiaojing Cai & Shigeyuki Hamori & Haifeng Xu, 2020. "Moving average threshold heterogeneous autoregressive (MAT‐HAR) models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1035-1042, November.
    17. Jinyuan Chang & Qing Jiang & Xiaofeng Shao, 2022. "Testing the martingale difference hypothesis in high dimension," Papers 2209.04770, arXiv.org, revised Sep 2022.
    18. Guay, Alain & Guerre, Emmanuel & Lazarová, Štěpána, 2013. "Robust adaptive rate-optimal testing for the white noise hypothesis," Journal of Econometrics, Elsevier, vol. 176(2), pages 134-145.
    19. Chen, Min & Zhu, Ke, 2014. "Sign-based specification tests for martingale difference with conditional heteroscedasity," MPRA Paper 56347, University Library of Munich, Germany.
    20. Li, Muyi & Zhang, Yanfen, 2022. "Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    21. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    22. Joseph P. Romano & Marius A. Tirlea, 2022. "Permutation testing for dependence in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 781-807, September.
    23. Characiejus, Vaidotas & Rice, Gregory, 2020. "A general white noise test based on kernel lag-window estimates of the spectral density operator," Econometrics and Statistics, Elsevier, vol. 13(C), pages 175-196.
    24. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    25. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    26. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    27. Ulrich Hounyo & Rasmus T. Varneskov, 2018. "Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach," CREATES Research Papers 2018-16, Department of Economics and Business Economics, Aarhus University.

  22. Shao, Xiaofeng, 2010. "Nonstationarity-Extended Whittle Estimation," Econometric Theory, Cambridge University Press, vol. 26(4), pages 1060-1087, August.

    Cited by:

    1. Artiach, Miguel & Arteche, Josu, 2012. "Doubly fractional models for dynamic heteroscedastic cycles," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2139-2158.
    2. Morten Ørregaard Nielsen, 2014. "Asymptotics for the conditional-sum-of-squares estimator in multivariate fractional time series models," CREATES Research Papers 2014-34, Department of Economics and Business Economics, Aarhus University.
    3. de Truchis, Gilles, 2013. "Approximate Whittle analysis of fractional cointegration and the stock market synchronization issue," Economic Modelling, Elsevier, vol. 34(C), pages 98-105.
    4. Yaya, OlaOluwa S & Gil-Alana, Luis A., 2018. "High and Low Intraday Commodity Prices: A Fractional Integration and Cointegration Approach," MPRA Paper 90518, University Library of Munich, Germany.
    5. Cheung, Ying Lun, 2020. "Nonstationarity-extended Whittle estimation with discontinuity: A correction," Economics Letters, Elsevier, vol. 187(C).
    6. Gil-Alana, Luis Alberiko & Trani, Tommaso, 2019. "An examination of trade-weighted real exchange rates based on fractional integration," International Economics, Elsevier, vol. 158(C), pages 64-76.
    7. Peter C. B. Phillips, 2023. "Discrete Fourier Transforms of Fractional Processes with Econometric Applications," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Theory, volume 45, pages 3-71, Emerald Group Publishing Limited.
    8. J -P Kreiss & E Paparoditis, 2023. "Bootstrapping Whittle estimators," Biometrika, Biometrika Trust, vol. 110(2), pages 499-518.
    9. Ying Lun Cheung & Uwe Hassler, 2020. "Whittle-type estimation under long memory and nonstationarity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 363-383, September.
    10. Guglielmo Maria Caporale & Luis A. Gil-Alana & Kefei You, 2019. "Stock market linkages between the ASEAN countries, China and the US: a fractional cointegration approach," CESifo Working Paper Series 7537, CESifo.
    11. Peter C.B. Phillips, 2021. "Discrete Fourier Transforms of Fractional Processes with Econometric Applications," Cowles Foundation Discussion Papers 2303, Cowles Foundation for Research in Economics, Yale University.

  23. Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.

    Cited by:

    1. Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
    2. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Timothy G. Conley & Sílvia Gonçalves & Min Seong Kim & Benoit Perron, 2023. "Bootstrap inference under cross‐sectional dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 511-569, May.
    4. Lea Wegner & Martin Wendler, 2024. "Robust change-point detection for functional time series based on U-statistics and dependent wild bootstrap," Statistical Papers, Springer, vol. 65(7), pages 4767-4810, September.
    5. Bulat Gafarov & Madina Karamysheva & Andrey Polbin & Anton Skrobotov, 2024. "Wild inference for wild SVARs with application to heteroscedasticity-based IV," Papers 2407.03265, arXiv.org, revised Nov 2024.
    6. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    7. Yu, Deshui & Chen, Li, 2024. "Local predictability of stock returns and cash flows," Journal of Empirical Finance, Elsevier, vol. 77(C).
    8. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 269-271, May.
    9. Steland, Ansgar, 2024. "Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    10. Denis Kojevnikov, 2021. "The Bootstrap for Network Dependent Processes," Papers 2101.12312, arXiv.org.
    11. Rajapaksha, Dilini & Bergmeir, Christoph & Hyndman, Rob J., 2023. "LoMEF: A framework to produce local explanations for global model time series forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1424-1447.
    12. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    13. Dovonon, Prosper & Goncalves, Silvia & Hounyo, Ulrich & Meddahi, Nour, 2017. "Bootstrapping high-frequency jump tests," IDEI Working Papers 870, Institut d'Économie Industrielle (IDEI), Toulouse.
    14. Axel Bücher & Holger Dette & Florian Heinrichs, 2023. "A portmanteau-type test for detecting serial correlation in locally stationary functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 255-278, July.
    15. Yeonwoo Rho & Xiaofeng Shao, 2018. "Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors," Papers 1802.05333, arXiv.org.
    16. Pang Du & Christopher F. Parmeter & Jeffrey S. Racine, 2012. "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Department of Economics Working Papers 2012-08, McMaster University.
    17. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.
    18. Leucht, Anne & Neumann, Michael H., 2013. "Dependent wild bootstrap for degenerate U- and V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 257-280.
    19. Bucchia, Béatrice & Wendler, Martin, 2017. "Change-point detection and bootstrap for Hilbert space valued random fields," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 344-368.
    20. Gu, Jingping & Liang, Zhongwen, 2014. "Testing cointegration relationship in a semiparametric varying coefficient model," Journal of Econometrics, Elsevier, vol. 178(P1), pages 57-70.
    21. Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
    22. Doukhan, Paul & Lang, Gabriel & Leucht, Anne & Neumann, Michael H., 2014. "Dependent wild bootstrap for the empirical process," Working Papers 35246, University of Mannheim, Department of Economics.
    23. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    24. Ying Wang & Peter C. B. Phillips & Yundong Tu, 2024. "Limit Theory and Inference in Non-cointegrated Functional Coefficient Regression," Cowles Foundation Discussion Papers 2399, Cowles Foundation for Research in Economics, Yale University.
    25. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.
    26. Bucher, Axel & Kojadinovic, Ivan, 2013. "A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing," LIDAM Discussion Papers ISBA 2013029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    27. Giuseppe Cavaliere & Iliyan Georgiev & A. M. Robert Taylor, 2013. "Wild Bootstrap of the Sample Mean in the Infinite Variance Case," Econometric Reviews, Taylor & Francis Journals, vol. 32(2), pages 204-219, February.
    28. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Nonparametric modeling for the time-varying persistence of inflation," Economics Letters, Elsevier, vol. 225(C).
    29. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
    30. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    31. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Srijan Sengupta & Xiaofeng Shao & Yingchuan Wang, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 315-326, May.
    32. Kaiji Motegi & Xiaojing Cai & Shigeyuki Hamori & Haifeng Xu, 2020. "Moving average threshold heterogeneous autoregressive (MAT‐HAR) models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1035-1042, November.
    33. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    34. Lee, Taewook & Baek, Changryong, 2020. "Block wild bootstrap-based CUSUM tests robust to high persistence and misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    35. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
    36. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    37. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    38. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    39. Hounyo, Ulrich & Varneskov, Rasmus T., 2017. "A local stable bootstrap for power variations of pure-jump semimartingales and activity index estimation," Journal of Econometrics, Elsevier, vol. 198(1), pages 10-28.
    40. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    41. Chen, Bin, 2015. "Modeling and testing smooth structural changes with endogenous regressors," Journal of Econometrics, Elsevier, vol. 185(1), pages 196-215.
    42. Michal Pešta & Martin Wendler, 2020. "Nuisance-parameter-free changepoint detection in non-stationary series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 379-408, June.
    43. Meintanis, Simos G. & Hušková, Marie & Hlávka, Zdeněk, 2022. "Fourier-type tests of mutual independence between functional time series," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    44. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    45. Hiroyuki Kawakatsu, 2022. "Local projection variance impulse response," Empirical Economics, Springer, vol. 62(3), pages 1219-1244, March.
    46. Ulrich Hounyo & Rasmus T. Varneskov, 2018. "Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach," CREATES Research Papers 2018-16, Department of Economics and Business Economics, Aarhus University.
    47. Ulrich Hounyo & Rasmus T. Varneskov, 2015. "A Local Stable Bootstrap for Power Variations of Pure-Jump Semimartingales and Activity Index Estimation," CREATES Research Papers 2015-26, Department of Economics and Business Economics, Aarhus University.
    48. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    49. Ulrich Hounyo & Kajal Lahiri, 2023. "Are Some Forecasters Really Better than Others? A Note," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 577-593, March.
    50. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Time-varying predictability of the long horizon equity premium based on semiparametric regressions," Economics Letters, Elsevier, vol. 224(C).
    51. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    52. Axel Bücher & Ivan Kojadinovic, 2019. "A Note on Conditional Versus Joint Unconditional Weak Convergence in Bootstrap Consistency Results," Journal of Theoretical Probability, Springer, vol. 32(3), pages 1145-1165, September.
    53. Lechuan Hu & Norbert J. Fortin & Hernando Ombao, 2019. "Modeling High-Dimensional Multichannel Brain Signals," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 91-126, April.
    54. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.

  24. Xiaofeng Shao, 2010. "A self‐normalized approach to confidence interval construction in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 343-366, June.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
    3. Jiang, Feiyu & Wang, Runmin & Shao, Xiaofeng, 2023. "Robust inference for change points in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    4. Sun, Jiajing & Hong, Yongmiao & Linton, Oliver & Zhao, Xiaolu, 2022. "Adjusted-range self-normalized confidence interval construction for censored dependent data," Economics Letters, Elsevier, vol. 220(C).
    5. Sun, Yixiao, 2013. "Fixed-smoothing Asymptotics in a Two-step GMM Framework," University of California at San Diego, Economics Working Paper Series qt64x4z265, Department of Economics, UC San Diego.
    6. Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    7. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    8. Nordman, Daniel J. & Bunzel, Helle & Lahiri, Soumendra N., 2013. "A Nonstandard Empirical Likelihood for Time Series," Staff General Research Papers Archive 37203, Iowa State University, Department of Economics.
    9. Xianyang Zhang & Bo Li & Xiaofeng Shao, 2014. "Self-normalization for Spatial Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 311-324, June.
    10. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    11. Lin Fan & Peter W. Glynn & Markus Pelger, 2018. "Change-Point Testing for Risk Measures in Time Series," Papers 1809.02303, arXiv.org, revised Jul 2023.
    12. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    13. Yi-Ting Chen & Zhongjun Qu, 2015. "M Tests with a New Normalization Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 617-652, May.
    14. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    15. Xuexin Wang & Yixiao Sun, 2020. "An Asymptotic F Test for Uncorrelatedness in the Presence of Time Series Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 536-550, July.
    16. Zhang, Jingsi & Jiang, Wenxin & Shao, Xiaofeng, 2013. "Bayesian model selection based on parameter estimates from subsamples," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 979-986.
    17. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    18. Zhang, Xianyang & Shao, Xiaofeng, 2013. "On a general class of long run variance estimators," Economics Letters, Elsevier, vol. 120(3), pages 437-441.
    19. Yeonwoo Rho & Xiaofeng Shao, 2015. "Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 444-457, July.
    20. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    21. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    22. Chen, Willa W. & Deo, Rohit S., 2018. "Subsampling based inference for U statistics under thick tails using self-normalization," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 95-103.
    23. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.
    24. Uribe, Jorge M. & Guillen, Montserrat & Mosquera-López, Stephania, 2018. "Uncovering the nonlinear predictive causality between natural gas and electricity prices," Energy Economics, Elsevier, vol. 74(C), pages 904-916.
    25. RMI staff article, 2016. "NUS-RMI Credit Research Initiative Technical Report Version: 2016 Update 1," Global Credit Review (GCR), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 49-132.
    26. Seong Yeon Chang & Pierre Perron, 2014. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Boston University - Department of Economics - Working Papers Series wp2015-011, Boston University - Department of Economics, revised 20 Sep 2015.
    27. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    28. Aigner, Maximilian & Chavez-Demoulin, Valérie & Guillou, Armelle, 2022. "Measuring and comparing risks of different types," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 1-21.
    29. Zhang, Xianyang, 2016. "Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework," Journal of Econometrics, Elsevier, vol. 193(1), pages 123-146.
    30. Nick Kloodt & Natalie Neumeyer & Ingrid Keilegom, 2021. "Specification testing in semi-parametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 980-1003, December.
    31. Axel Bücher, 2015. "A Note on Weak Convergence of the Sequential Multivariate Empirical Process Under Strong Mixing," Journal of Theoretical Probability, Springer, vol. 28(3), pages 1028-1037, September.
    32. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    33. Wenger, Kai & Less, Vivien, 2020. "A modified Wilcoxon test for change points in long-range dependent time series," Economics Letters, Elsevier, vol. 192(C).
    34. Holger Dette & Kevin Kokot & Stanislav Volgushev, 2020. "Testing relevant hypotheses in functional time series via self‐normalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 629-660, July.
    35. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

  25. Shao, Xiaofeng & Zhang, Xianyang, 2010. "Testing for Change Points in Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1228-1240.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Jiang, Feiyu & Wang, Runmin & Shao, Xiaofeng, 2023. "Robust inference for change points in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    3. Steland, Ansgar, 2024. "Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    4. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.
    5. Alessandro Casini & Pierre Perron, 2021. "Prewhitened Long-Run Variance Estimation Robust to Nonstationarity," Papers 2103.02235, arXiv.org, revised Aug 2024.
    6. Liu, Bin & Zhou, Cheng & Zhang, Xinsheng, 2019. "A tail adaptive approach for change point detection," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 33-48.
    7. Annika Betken, 2016. "Testing for Change-Points in Long-Range Dependent Time Series by Means of a Self-Normalized Wilcoxon Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 785-809, November.
    8. Sun, Jiajing & Hong, Yongmiao & Linton, Oliver & Zhao, Xiaolu, 2022. "Adjusted-range self-normalized confidence interval construction for censored dependent data," Economics Letters, Elsevier, vol. 220(C).
    9. Wingert, Simon & Mboya, Mwasi Paza & Sibbertsen, Philipp, 2020. "Distinguishing between breaks in the mean and breaks in persistence under long memory," Economics Letters, Elsevier, vol. 193(C).
    10. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    11. Buddhananda Banerjee & Satyaki Mazumder, 2018. "A more powerful test identifying the change in mean of functional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 691-715, June.
    12. JIANG, Peiyun & 蒋, 佩芸 & KUROZUMI, Eiji & 黒住, 英司, 2017. "Power Properties of the Modified CUSUM Tests," Discussion Papers 2017-05, Graduate School of Economics, Hitotsubashi University.
    13. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
    14. Han Lin Shang & Jiguo Cao & Peijun Sang, 2022. "Stopping time detection of wood panel compression: A functional time‐series approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1205-1224, November.
    15. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    16. Lin Fan & Peter W. Glynn & Markus Pelger, 2018. "Change-Point Testing for Risk Measures in Time Series," Papers 1809.02303, arXiv.org, revised Jul 2023.
    17. Josephine Njeri Ngure & Anthony Gichuhi Waititu, 2021. "Consistency of an Estimator for Change Point in Volatility of Financial Returns," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 13(1), pages 1-56, February.
    18. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    19. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    20. Alfredas Račkauskas & Martin Wendler, 2020. "Convergence of U-processes in Hölder spaces with application to robust detection of a changed segment," Statistical Papers, Springer, vol. 61(4), pages 1409-1435, August.
    21. Matúš Maciak & Sebastiano Vitali, 2024. "Using interpolated implied volatility for analysing exogenous market changes," Computational Management Science, Springer, vol. 21(1), pages 1-21, June.
    22. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    23. Mengchen Wang & Trevor Harris & Bo Li, 2023. "Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 157-176, March.
    24. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    25. YAMAZAKI, Daisuke & 山崎, 大輔 & KUROZUMI, Eiji & 黒住, 英司, 2014. "Improving the Finite Sample Performance of Tests for a Shift in Mean," Discussion Papers 2014-16, Graduate School of Economics, Hitotsubashi University.
    26. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    27. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    28. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    29. Trevor Harris & Bo Li & J. Derek Tucker, 2022. "Scalable multiple changepoint detection for functional data sequences," Environmetrics, John Wiley & Sons, Ltd., vol. 33(2), March.
    30. Michal Pešta, 2021. "Changepoint in Error-Prone Relations," Mathematics, MDPI, vol. 9(1), pages 1-25, January.
    31. Cho, Haeran & Fryzlewicz, Piotr, 2023. "Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm," LSE Research Online Documents on Economics 120085, London School of Economics and Political Science, LSE Library.
    32. Ting Wang & Benjamin Graves & Yves Rosseel & Edgar C. Merkle, 2022. "Computation and application of generalized linear mixed model derivatives using lme4," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1173-1193, September.
    33. Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    34. Bin Liu & Cheng Zhou & Xinsheng Zhang & Yufeng Liu, 2020. "A unified data‐adaptive framework for high dimensional change point detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 933-963, September.
    35. Michal Pešta & Martin Wendler, 2020. "Nuisance-parameter-free changepoint detection in non-stationary series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 379-408, June.
    36. Ngai Hang Chan & Chun Yip Yau & Rong-Mao Zhang, 2014. "Group LASSO for Structural Break Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 590-599, June.
    37. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    38. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.
    39. Y Hoga, 2018. "A structural break test for extremal dependence in β-mixing random vectors," Biometrika, Biometrika Trust, vol. 105(3), pages 627-643.
    40. Josua Gösmann & Tobias Kley & Holger Dette, 2021. "A new approach for open‐end sequential change point monitoring," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 63-84, January.
    41. Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
    42. Jiang, Peiyun & Kurozumi, Eiji, 2021. "A new test for common breaks in heterogeneous panel data models," Discussion paper series HIAS-E-107, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    43. Nick Kloodt & Natalie Neumeyer & Ingrid Keilegom, 2021. "Specification testing in semi-parametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 980-1003, December.
    44. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    45. Liu, Bin & Zhang, Xinsheng & Liu, Yufeng, 2022. "High dimensional change point inference: Recent developments and extensions," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    46. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2017. "Dynamic semiparametric factor model with a common break," SFB 649 Discussion Papers 2017-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    47. Xu, Haotian & Wang, Daren & Zhao, Zifeng & Yu, Yi, 2022. "Change point inference in high-dimensional regression models under temporal dependence," LIDAM Discussion Papers ISBA 2022027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    48. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    49. Holger Dette & Kevin Kokot & Stanislav Volgushev, 2020. "Testing relevant hypotheses in functional time series via self‐normalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 629-660, July.

  26. Xiaofeng Shao, 2010. "Corrigendum: A self‐normalized approach to confidence interval construction in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 695-696, November.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
    3. Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    4. Hong, Y. & Linton, O. B. & McCabe, B. & Sun, J. & Wang, S., 2023. "Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach," Janeway Institute Working Papers 2316, Faculty of Economics, University of Cambridge.
    5. Xianyang Zhang & Bo Li & Xiaofeng Shao, 2014. "Self-normalization for Spatial Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 311-324, June.
    6. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    7. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    8. Yi-Ting Chen & Zhongjun Qu, 2015. "M Tests with a New Normalization Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 617-652, May.
    9. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    10. Xuexin Wang & Yixiao Sun, 2020. "An Asymptotic F Test for Uncorrelatedness in the Presence of Time Series Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 536-550, July.
    11. Zhang, Jingsi & Jiang, Wenxin & Shao, Xiaofeng, 2013. "Bayesian model selection based on parameter estimates from subsamples," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 979-986.
    12. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    13. Zhang, Xianyang & Shao, Xiaofeng, 2013. "On a general class of long run variance estimators," Economics Letters, Elsevier, vol. 120(3), pages 437-441.
    14. Yeonwoo Rho & Xiaofeng Shao, 2015. "Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 444-457, July.
    15. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    16. Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    17. Chen, Willa W. & Deo, Rohit S., 2018. "Subsampling based inference for U statistics under thick tails using self-normalization," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 95-103.
    18. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.
    19. Uribe, Jorge M. & Guillen, Montserrat & Mosquera-López, Stephania, 2018. "Uncovering the nonlinear predictive causality between natural gas and electricity prices," Energy Economics, Elsevier, vol. 74(C), pages 904-916.
    20. RMI staff article, 2016. "NUS-RMI Credit Research Initiative Technical Report Version: 2016 Update 1," Global Credit Review (GCR), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 49-132.
    21. Seong Yeon Chang & Pierre Perron, 2014. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Boston University - Department of Economics - Working Papers Series wp2015-011, Boston University - Department of Economics, revised 20 Sep 2015.
    22. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    23. Zhang, Xianyang, 2016. "Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework," Journal of Econometrics, Elsevier, vol. 193(1), pages 123-146.
    24. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    25. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

  27. Shao, Xiaofeng, 2009. "A Generalized Portmanteau Test For Independence Between Two Stationary Time Series," Econometric Theory, Cambridge University Press, vol. 25(1), pages 195-210, February.

    Cited by:

    1. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    2. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
    3. 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.
    4. 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.
    5. Hao, Jing & He, Feng, 2018. "Univariate dependence among sectors in Chinese stock market and systemic risk implication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 355-364.

  28. Xiaofeng Shao, 2009. "Confidence intervals for spectral mean and ratio statistics," Biometrika, Biometrika Trust, vol. 96(1), pages 107-117.

    Cited by:

    1. Zhang, Xinyu & Tong, Howell, 2022. "Asymptotic theory of principal component analysis for time series data with cautionary comments," LSE Research Online Documents on Economics 113566, London School of Economics and Political Science, LSE Library.
    2. Xinyu Zhang & Howell Tong, 2022. "Asymptotic theory of principal component analysis for time series data with cautionary comments," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 543-565, April.
    3. Xiaofeng Shao, 2010. "A self‐normalized approach to confidence interval construction in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 343-366, June.

  29. Wu, Wei Biao & Shao, Xiaofeng, 2007. "A Limit Theorem For Quadratic Forms And Its Applications," Econometric Theory, Cambridge University Press, vol. 23(5), pages 930-951, October.

    Cited by:

    1. Zhongjun Qu, 2010. "A Test Against Spurious Long Memory," Boston University - Department of Economics - Working Papers Series WP2010-051, Boston University - Department of Economics.
    2. Pavel Yaskov, 2018. "LLN for Quadratic Forms of Long Memory Time Series and Its Applications in Random Matrix Theory," Journal of Theoretical Probability, Springer, vol. 31(4), pages 2032-2055, December.
    3. Zhengyan Lin & Hanchao Wang, 2016. "On Convergence to Stochastic Integrals," Journal of Theoretical Probability, Springer, vol. 29(3), pages 717-736, September.
    4. Jirak, Moritz, 2013. "A Darling–Erdös type result for stationary ellipsoids," Stochastic Processes and their Applications, Elsevier, vol. 123(6), pages 1922-1946.
    5. van Delft, Anne, 2020. "A note on quadratic forms of stationary functional time series under mild conditions," Stochastic Processes and their Applications, Elsevier, vol. 130(7), pages 4206-4251.
    6. Zhang, Tonglin, 2019. "General Gaussian estimation," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 234-247.
    7. Zhang, Rong-Mao & Sin, Chor-yiu (CY) & Ling, Shiqing, 2015. "On functional limits of short- and long-memory linear processes with GARCH(1,1) noises," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 482-512.
    8. Atchadé, Yves F. & Cattaneo, Matias D., 2014. "A martingale decomposition for quadratic forms of Markov chains (with applications)," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 646-677.
    9. 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.
    10. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    11. Chan, Ngai Hang & Zhang, Rong-Mao, 2013. "Limit theory of quadratic forms of long-memory linear processes with heavy-tailed GARCH innovations," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 18-33.
    12. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    13. Yi He & Sombut Jaidee & Jiti Gao, 2020. "Most Powerful Test against High Dimensional Free Alternatives," Monash Econometrics and Business Statistics Working Papers 13/20, Monash University, Department of Econometrics and Business Statistics.
    14. Ángel Felipe & María Jaenada & Pedro Miranda & Leandro Pardo, 2023. "Restricted Distance-Type Gaussian Estimators Based on Density Power Divergence and Their Applications in Hypothesis Testing," Mathematics, MDPI, vol. 11(6), pages 1-41, March.
    15. Kin Wai Chan & Chun Yip Yau, 2017. "High-order Corrected Estimator of Asymptotic Variance with Optimal Bandwidth," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 866-898, December.

  30. Shao, Xiaofeng & Wu, Wei Biao, 2007. "Local Whittle Estimation Of Fractional Integration For Nonlinear Processes," Econometric Theory, Cambridge University Press, vol. 23(5), pages 899-929, October.

    Cited by:

    1. Zhongjun Qu, 2010. "A Test Against Spurious Long Memory," Boston University - Department of Economics - Working Papers Series WP2010-051, Boston University - Department of Economics.
    2. Alessandro Casini & Pierre Perron, 2021. "Change-Point Analysis of Time Series with Evolutionary Spectra," Papers 2106.02031, arXiv.org, revised Aug 2024.
    3. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.
    4. La Spada Gabriele & Lillo Fabrizio, 2014. "The effect of round-off error on long memory processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 445-482, September.
    5. Arteche, Josu & Orbe, Jesus, 2017. "A strategy for optimal bandwidth selection in Local Whittle estimation," Econometrics and Statistics, Elsevier, vol. 4(C), pages 3-17.
    6. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Fractional Integration and Cointegration in US Financial Time Series Data," Discussion Papers of DIW Berlin 1116, DIW Berlin, German Institute for Economic Research.
    7. de Menezes, Lilian M. & Houllier, Melanie A., 2015. "Germany's nuclear power plant closures and the integration of electricity markets in Europe," Energy Policy, Elsevier, vol. 85(C), pages 357-368.
    8. Shimotsu, Katsumi & 下津, 克己, 2010. "Exact Local Whittle Estimation of Fractionally Cointegrated Systems," Discussion Papers 2010-11, Graduate School of Economics, Hitotsubashi University.
    9. Giuseppe Cavaliere & Morten Ørregaard Nielsen & Robert Taylor, 2020. "Adaptive Inference in Heteroskedastic Fractional Time Series Models," CREATES Research Papers 2020-08, Department of Economics and Business Economics, Aarhus University.
    10. Estefania Mourelle & Juan Carlos Cuestas & Luis Alberiko Gil‐alana, 2011. "Is There An Asymmetric Behaviour In African Inflation? A Non‐Linear Approach," South African Journal of Economics, Economic Society of South Africa, vol. 79(1), pages 68-90, March.
    11. Hailin Sang & Yongli Sang, 2017. "Memory properties of transformations of linear processes," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 79-103, April.
    12. Arteche González, Jesús María, 2020. "Frequency Domain Local Bootstrap in long memory time series," BILTOKI info:eu-repo/grantAgreeme, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    13. Arteche, Josu, 2024. "Bootstrapping long memory time series: Application in low frequency estimators," Econometrics and Statistics, Elsevier, vol. 29(C), pages 1-15.
    14. Lilian de Menezes & Melanie A. Houllier, 2013. "Modelling Germany´s Energy Transition and its Potential Effect on European Electricity Spot Markets," EcoMod2013 5395, EcoMod.
    15. Ying Lun Cheung & Uwe Hassler, 2020. "Whittle-type estimation under long memory and nonstationarity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 363-383, September.

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