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Testing stationarity of functional time series

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Cited by:

  1. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
  2. 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.
  3. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  4. Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2022. "Bayesian Testing of Granger Causality in Functional Time Series," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 191-210, September.
  5. Horváth, Lajos & Rice, Gregory & Whipple, Stephen, 2016. "Adaptive bandwidth selection in the long run covariance estimator of functional time series," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 676-693.
  6. Gregory Rice & Tony Wirjanto & Yuqian Zhao, 2020. "Tests for conditional heteroscedasticity of functional data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 733-758, November.
  7. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
  8. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
  9. Casini, Alessandro & Perron, Pierre, 2024. "Change-point analysis of time series with evolutionary spectra," Journal of Econometrics, Elsevier, vol. 242(2).
  10. Ajroldi, Niccolò & Diquigiovanni, Jacopo & Fontana, Matteo & Vantini, Simone, 2023. "Conformal prediction bands for two-dimensional functional time series," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  11. Salish, Nazarii & Gleim, Alexander, 2019. "A moment-based notion of time dependence for functional time series," Journal of Econometrics, Elsevier, vol. 212(2), pages 377-392.
  12. Haixu Wang & Jiguo Cao, 2023. "Nonlinear prediction of functional time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
  13. Oomen, Roel, 2018. "Price signatures," LSE Research Online Documents on Economics 90481, London School of Economics and Political Science, LSE Library.
  14. Nielsen, Morten Ørregaard & Seo, Won-Ki & Seong, Dakyung, 2023. "Inference On The Dimension Of The Nonstationary Subspace In Functional Time Series," Econometric Theory, Cambridge University Press, vol. 39(3), pages 443-480, June.
  15. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  16. Farzad Sabzikar & Piotr Kokoszka, 2023. "Tempered functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 280-293, May.
  17. Maeng, Hye Young & Fryzlewicz, Piotr, 2019. "Regularised forecasting via smooth-rough partitioning of the regression coefficients," LSE Research Online Documents on Economics 100878, London School of Economics and Political Science, LSE Library.
  18. Amira Elayouty & Marian Scott & Claire Miller, 2022. "Time-Varying Functional Principal Components for Non-Stationary EpCO $$_2$$ 2 in Freshwater Systems," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 506-522, September.
  19. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01821815, HAL.
  20. Rodney V. Fonseca & Aluísio Pinheiro, 2020. "Wavelet estimation of the dimensionality of curve time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1175-1204, October.
  21. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  22. Horváth, Lajos & Rice, Gregory, 2015. "Testing for independence between functional time series," Journal of Econometrics, Elsevier, vol. 189(2), pages 371-382.
  23. 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.
  24. Israel Martínez‐Hernández & Marc G. Genton, 2021. "Nonparametric trend estimation in functional time series with application to annual mortality rates," Biometrics, The International Biometric Society, vol. 77(3), pages 866-878, September.
  25. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
  26. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).
  27. Maciej Jagódka & Małgorzata Snarska, 2023. "Should We Continue EU Cohesion Policy? The Dilemma of Uneven Development of Polish Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(3), pages 901-917, February.
  28. Degui Li & Peter M. Robinson & Han Lin Shang, 2021. "Local Whittle estimation of long‐range dependence for functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 685-695, September.
  29. Stephanie Hoffmann & Susanne Schulze & Antje Löffler & Juliane Becker & Frank Hufert & Heinz-Detlef Gremmels & Christine Holmberg & Michael A Rapp & Sonja Entringer & Jacob Spallek, 2024. "Did the prevalence of depressive symptoms change during the COVID-19 pandemic? A multilevel analysis on longitudinal data from healthcare workers," International Journal of Social Psychiatry, , vol. 70(1), pages 87-98, February.
  30. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2023. "Exploring volatility of crude oil intraday return curves: A functional GARCH-X model," Journal of Commodity Markets, Elsevier, vol. 32(C).
  31. Bo Li & Sabri Boubaker & Zhenya Liu & Waël Louhichi & Yao Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 527-559, August.
  32. Dominique Guégan & Matteo Iacopini, 2018. "Nonparameteric forecasting of multivariate probability density functions," Documents de travail du Centre d'Economie de la Sorbonne 18012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  33. Liu, Yirui & Qiao, Xinghao & Pei, Yulong & Wang, Liying, 2024. "Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization," LSE Research Online Documents on Economics 125587, London School of Economics and Political Science, LSE Library.
  34. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2021. "Exploring volatility of crude oil intra-day return curves: a functional GARCH-X Model," MPRA Paper 109231, University Library of Munich, Germany.
  35. Han Lin Shang & Kaiying Ji & Ufuk Beyaztas, 2021. "Granger causality of bivariate stationary curve time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 626-635, July.
  36. Qiang Ji & Dayong Zhang & Yuqian Zhao, 2022. "Intra-day co-movements of crude oil futures: China and the international benchmarks," Annals of Operations Research, Springer, vol. 313(1), pages 77-103, June.
  37. Matteo Iacopini & Dominique Guégan, 2018. "Nonparametric Forecasting of Multivariate Probability Density Functions," Working Papers 2018:15, Department of Economics, University of Venice "Ca' Foscari".
  38. Cerovecki, Clément & Francq, Christian & Hörmann, Siegfried & Zakoïan, Jean-Michel, 2019. "Functional GARCH models: The quasi-likelihood approach and its applications," Journal of Econometrics, Elsevier, vol. 209(2), pages 353-375.
  39. Niccol`o Ajroldi & Jacopo Diquigiovanni & Matteo Fontana & Simone Vantini, 2022. "Conformal Prediction Bands for Two-Dimensional Functional Time Series," Papers 2207.13656, arXiv.org, revised Jul 2023.
  40. Daniel Kosiorowski & Jerzy P. Rydlewski & Ma{l}gorzata Snarska, 2016. "Detecting a Structural Change in Functional Time Series Using Local Wilcoxon Statistic," Papers 1604.03776, arXiv.org, revised Oct 2019.
  41. 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.
  42. Li, Xuemei & Liu, Xiaoxing, 2023. "Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures," Energy, Elsevier, vol. 284(C).
  43. Chang, Jinyuan & Chen, Cheng & Qiao, Xinghao & Yao, Qiwei, 2023. "An autocovariance-based learning framework for high-dimensional functional time series," LSE Research Online Documents on Economics 117910, London School of Economics and Political Science, LSE Library.
  44. Morten {O}rregaard Nielsen & Won-Ki Seo & Dakyung Seong, 2023. "Inference on common trends in functional time series," Papers 2312.00590, arXiv.org, revised May 2024.
  45. Zhao, Yuqian, 2021. "Validating intra-day risk premium in cross-sectional return curves," Finance Research Letters, Elsevier, vol. 43(C).
  46. Kokoszka Piotr & Miao Hong & Zheng Ben, 2017. "Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 33-53, June.
  47. Han Lin Shang & Kaiying Ji, 2023. "Forecasting intraday financial time series with sieve bootstrapping and dynamic updating," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1973-1988, December.
  48. 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).
  49. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2019. "Tests for conditional heteroscedasticity with functional data and goodness-of-fit tests for FGARCH models," MPRA Paper 93048, University Library of Munich, Germany.
  50. Yuan Gao & Han Lin Shang, 2017. "Multivariate Functional Time Series Forecasting: Application to Age-Specific Mortality Rates," Risks, MDPI, vol. 5(2), pages 1-18, March.
  51. Klepsch, J. & Klüppelberg, C. & Wei, T., 2017. "Prediction of functional ARMA processes with an application to traffic data," Econometrics and Statistics, Elsevier, vol. 1(C), pages 128-149.
  52. Han Lin Shang, 2024. "Bootstrapping Long-Run Covariance of Stationary Functional Time Series," Forecasting, MDPI, vol. 6(1), pages 1-14, February.
  53. Martínez-Hernández, Israel & Genton, Marc G. & González-Farías, Graciela, 2019. "Robust depth-based estimation of the functional autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 66-79.
  54. Shang, Han Lin, 2017. "Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration," Econometrics and Statistics, Elsevier, vol. 1(C), pages 184-200.
  55. Guochang Wang & Zengyao Wen & Shanming Jia & Shanshan Liang, 2024. "Supervised dimension reduction for functional time series," Statistical Papers, Springer, vol. 65(7), pages 4057-4077, September.
  56. Cerovecki, Clément & Hörmann, Siegfried, 2017. "On the CLT for discrete Fourier transforms of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 282-295.
  57. Yizheng Fu & Zhifang Su & Aihua Lin, 2024. "Functional Cointegration Test for Expectation Hypothesis of the Term Structure of Interest Rates in China," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(4), pages 799-820, December.
  58. Chao Zhang & Piotr Kokoszka & Alexander Petersen, 2022. "Wasserstein autoregressive models for density time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 30-52, January.
  59. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Post-Print halshs-01821815, HAL.
  60. Daniel Kosiorowski & Jerzy P. Rydlewski & Małgorzata Snarska, 2019. "Detecting a structural change in functional time series using local Wilcoxon statistic," Statistical Papers, Springer, vol. 60(5), pages 1677-1698, October.
  61. Bouri, Elie & Lau, Chi Keung Marco & Saeed, Tareq & Wang, Shixuan & Zhao, Yuqian, 2021. "On the intraday return curves of Bitcoin: Predictability and trading opportunities," International Review of Financial Analysis, Elsevier, vol. 76(C).
  62. 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.
  63. Fang, Qin & Guo, Shaojun & Qiao, Xinghao, 2022. "Finite sample theory for high-dimensional functional/scalar time series with applications," LSE Research Online Documents on Economics 114637, London School of Economics and Political Science, LSE Library.
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