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Lajos Horvath

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. Horváth, L. & Liu, Z. & Lu, S., 2021. "Sequential monitoring of changes in dynamic linear models, applied to the US housing market," Post-Print hal-03323683, HAL.

    Cited by:

    1. Boubaker, Sabri & Liu, Zhenya & Sui, Tianqing & Zhai, Ling, 2022. "The mirror of history: How to statistically identify stock market bubble bursts," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 128-147.
    2. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    3. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.
    4. 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.

  2. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.

    Cited by:

    1. Dettoni, Robinson & Gil-Alana, Luis Alberiko, 2023. "Testing the hypothesis of duration dependence in the U.S. housing market," Finance Research Letters, Elsevier, vol. 58(PD).
    2. Ye Chen & Jian Li & Qiyuan Li, 2023. "Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 910-937, August.
    3. Genoni, Giulia & Quatto, Piero & Vacca, Gianmarco, 2023. "Dating financial bubbles via online multiple testing procedures," Finance Research Letters, Elsevier, vol. 58(PA).
    4. Dettoni, Robinson & Gil-Alana, Luis A. & Yaya, OlaOluwa S., 2024. "Stock market prices and Dividends in the US: Bubbles or Long-run equilibria relationships?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. Potrykus, Marcin, 2023. "Investing in wine, precious metals and G-7 stock markets – A co-occurrence analysis for price bubbles," International Review of Financial Analysis, Elsevier, vol. 87(C).
    6. Aktham Maghyereh & Hussein Abdoh, 2022. "Bubble contagion effect between the main precious metals," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 40(1), pages 43-63, March.
    7. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).

  3. Ruanmin Cao & Lajos Horváth & Zhenya Liu & Yuqian Zhao, 2020. "A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis," Post-Print hal-03511284, HAL.

    Cited by:

    1. Mohamed S. Ahmed & John A. Doukas, 2021. "Revisiting disposition effect and momentum: a quantile regression perspective," Review of Quantitative Finance and Accounting, Springer, vol. 56(3), pages 1087-1128, April.
    2. Boubaker, Sabri & Liu, Zhenya & Lu, Shanglin & Zhang, Yifan, 2021. "Trading signal, functional data analysis and time series momentum," Finance Research Letters, Elsevier, vol. 42(C).
    3. Chen, Hong-Yi & Hsieh, Chia-Hsun & Lee, Cheng-Few, 2023. "Revisiting the momentum effect in Taiwan: The role of persistency," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    4. Zhao, Yuqian, 2021. "Validating intra-day risk premium in cross-sectional return curves," Finance Research Letters, Elsevier, vol. 43(C).

  4. Lajos Horváth & Zhenya Liu & Gregory Rice & Shixuan Wang, 2020. "A functional time series analysis of forward curves derived from commodity futures," Post-Print hal-03513421, HAL.

    Cited by:

    1. 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.
    2. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    3. 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).
    4. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Awasthi, Kritika & Ahmad, Wasim & Rahman, Abdul & Phani, B.V., 2020. "When US sneezes, clichés spread: How do the commodity index funds react then?," Resources Policy, Elsevier, vol. 69(C).

  5. Lajos Horvath & Lorenzo Trapani, 2018. "Testing for randomness in a random coefficient autoregression model," Discussion Papers 18/03, University of Nottingham, Granger Centre for Time Series Econometrics.

    Cited by:

    1. Trapani, Lorenzo, 2021. "A test for strict stationarity in a random coefficient autoregressive model of order 1," Statistics & Probability Letters, Elsevier, vol. 177(C).
    2. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    3. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2024. "Inference in Heavy-Tailed Nonstationary Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 565-581, January.
    4. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2021. "Tests for random coefficient variation in vector autoregressive models," Working Papers wp2021_2108, CEMFI.
    5. Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    6. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    7. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
    8. Mikihito Nishi, 2023. "Testing for Coefficient Randomness in Local-to-Unity Autoregressions," Papers 2301.04853, arXiv.org, revised Jan 2023.
    9. 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.

  6. Barassi, Marco & Horvath, Lajos & Zhao, Yuqian, 2018. "Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," MPRA Paper 87837, University Library of Munich, Germany.

    Cited by:

    1. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    2. Li, Hemei & Liu, Zhenya & Xiao, Zhijie, 2024. "Sequential monitoring of stock market price changes," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 156-172.
    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. 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.

  7. Hanousek, Jan & Antoch, Jaromir & Huskova, Marie & Horvath, Lajos & Wang, Shixuan, 2017. "Structural breaks in panel data: Large number of panels and short length time series," CEPR Discussion Papers 11891, C.E.P.R. Discussion Papers.

    Cited by:

    1. Argyropoulos, Christos & Candelon, Bertrand & Hasse, Jean-Baptiste & Panopoulou, Ekaterini, 2020. "Toward a macroprudential regulatory framework for mutual funds," LIDAM Discussion Papers LFIN 2020008, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Kraft, Kornelius & Lammers, Alexander, 2021. "The Effects of Reforming a Federal Employment Agency on Labor Demand," IZA Discussion Papers 14629, Institute of Labor Economics (IZA).
    3. Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2023. "Multiple Structural Breaks in Interactive Effects Panel Data and the Impact of Quantitative Easing on Bank Lending," BEMPS - Bozen Economics & Management Paper Series BEMPS99, Faculty of Economics and Management at the Free University of Bozen.
    4. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    5. 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.
    6. Daniel Ventosa‐Santaulària & Luis G. Hernández‐Román & Alejandro Villagómez Amezcua, 2021. "Recessions and potential GDP: The case of Mexico," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 179-195, April.
    7. Yiannis Karavias & Paresh Narayan & Joakim Westerlund, 2021. "Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19," Papers 2111.03035, arXiv.org.
    8. Klaudia Jarno & Hanna Kołodziejczyk, 2021. "Does the Design of Stablecoins Impact Their Volatility?," JRFM, MDPI, vol. 14(2), pages 1-14, January.
    9. Kraft, Kornelius & Lammers, Alexander, 2021. "Bargaining Power and the Labor Share - a Structural Break Approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242342, Verein für Socialpolitik / German Economic Association.
    10. Maran Marimuthu & Hanana Khan & Romana Bangash, 2021. "Reverse Causality between Fiscal and Current Account Deficits in ASEAN: Evidence from Panel Econometric Analysis," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    11. Jaromír Antoch & Jan Hanousek & Marie Hušková & Jiří Trešl, 2019. "Detekce změn v panelových datech: Změna parametrů Fama-French modelu u vybraných evropských akcií v období finanční krize [Detection of Changes in Panel Data: Change in Fama-French Model Parameters," Politická ekonomie, Prague University of Economics and Business, vol. 2019(1), pages 3-19.
    12. Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    13. Karamti, Chiraz & Jeribi, Ahmed, 2023. "Stock markets from COVID-19 to the Russia–Ukraine crisis: Structural breaks in interactive effects panels," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    14. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.

  8. Aue, Alexander & Horvath, Lajos & Pellatt, Daniel, 2015. "Functional generalized autoregressive conditional heteroskedasticity," MPRA Paper 67702, University Library of Munich, Germany.

    Cited by:

    1. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    2. 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.
    3. 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).
    4. Gregory Rice & Han Lin Shang, 2017. "A Plug-in Bandwidth Selection Procedure for Long-Run Covariance Estimation with Stationary Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 591-609, July.
    5. 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.
    6. Mihyun Kim & Piotr Kokoszka & Gregory Rice, 2024. "Projection-based white noise and goodness-of-fit tests for functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 27(3), pages 693-724, October.
    7. Rostyslav Bodnar & Taras Bodnar & Wolfgang Schmid, 2023. "Sequential monitoring of high‐dimensional time series," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 962-992, September.
    8. 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.
    9. Cerovecki, Clément & Francq, Christian & Hormann, Siegfried & Zakoian, Jean-Michel, 2018. "Functional GARCH models: the quasi-likelihood approach and its applications," MPRA Paper 83990, University Library of Munich, Germany.
    10. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2021. "Testing serial independence with functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 603-629, September.
    11. 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.
    12. 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.
    13. Marc Hallin & Gilles Nisol & Shahin Tavakoli, 2023. "Factor models for high‐dimensional functional time series I: Representation results," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 578-600, September.
    14. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 29-54, March.
    15. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    16. 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.
    17. Baye Matar Kandji, 2023. "On the growth rate of superadditive processes and the stability of functional GARCH models," Working Papers 2023-07, Center for Research in Economics and Statistics.
    18. 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).
    19. 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.
    20. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    21. Gao, Yuan & Shang, Han Lin & Yang, Yanrong, 2019. "High-dimensional functional time series forecasting: An application to age-specific mortality rates," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 232-243.
    22. Phillip A. Jang & David S. Matteson, 2023. "Spatial correlation in weather forecast accuracy: a functional time series approach," Computational Statistics, Springer, vol. 38(3), pages 1215-1229, September.

  9. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. Zhang, Yichen & Hurvich, Clifford M., 2022. "Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 972-994.

  10. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2014. "Variance targeting estimation of multivariate GARCH models," MPRA Paper 57794, University Library of Munich, Germany.

    Cited by:

    1. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Giuseppe Cavaliere & Rasmus Søndergaard Pedersen & Anders Rahbek, 2018. "The Fixed Volatility Bootstrap for a Class of Arch(q) Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 920-941, November.
    3. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    4. Barassi, Marco & Horvath, Lajos & Zhao, Yuqian, 2018. "Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," MPRA Paper 87837, University Library of Munich, Germany.
    5. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    6. So, Mike K.P. & Chan, Thomas W.C. & Chu, Amanda M.Y., 2022. "Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management," Journal of Econometrics, Elsevier, vol. 227(1), pages 151-167.
    7. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.
    8. Lai T. Hoang & Dirk G. Baur, 2021. "Spillovers and Asset Allocation," JRFM, MDPI, vol. 14(8), pages 1-31, July.
    9. Hetland, Simon & Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2023. "Dynamic conditional eigenvalue GARCH," Journal of Econometrics, Elsevier, vol. 237(2).
    10. Billio, Monica & Caporin, Massimiliano & Frattarolo, Lorenzo & Pelizzon, Loriana, 2018. "Networks in risk spillovers: A multivariate GARCH perspective," SAFE Working Paper Series 225, Leibniz Institute for Financial Research SAFE.
    11. Thieu, Le Quyen, 2016. "Variance targeting estimation of the BEKK-X model," MPRA Paper 75572, University Library of Munich, Germany.
    12. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
    13. Qi Li & Fukang Zhu, 2020. "Mean targeting estimator for the integer-valued GARCH(1, 1) model," Statistical Papers, Springer, vol. 61(2), pages 659-679, April.
    14. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Lyubimov, Ivan L. (Любимов, Иван) & Kazakova, Maria V. (Казакова, Мария), 2017. "The Demand for Production Inputs as the Reflection of the Level of Property Rights Protection [Структура Спроса На Факторы Производства Как Отражение Защищенности Прав Собственности]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 30-59, August.
    16. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.
    17. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling and Estimation," Papers 2206.14275, arXiv.org, revised Jan 2025.
    18. Loïc Cantin & Christian Francq & Jean-Michel Zakoïan, 2022. "Estimating dynamic systemic risk measures," Working Papers 2022-11, Center for Research in Economics and Statistics.

  11. Christian FRANCQ & Lajos HORVATH & Jean-Michel ZAKOIAN, 2009. "Sup-Tests for Linearity in a General Nonlinear AR(1) Model," Working Papers 2009-16, Center for Research in Economics and Statistics.

    Cited by:

    1. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.
    2. Yae Ji Jun & Jin Seo Cho, 2015. "Analyzing the Interrelationship of the Statistics for Testing Neglected Nonlinearity under the Null of Linearity," Working papers 2015rwp-78, Yonsei University, Yonsei Economics Research Institute.
    3. Wei-Wen Hsu & David Todem & Kyungmann Kim, 2015. "Adjusted Supremum Score-Type Statistics for Evaluating Non-Standard Hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 746-759, September.
    4. Christian Francq & Olivier Wintenberger & Jean-Michel Zakoïan, 2018. "Goodness-of-fit tests for Log-GARCH and EGARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 27-51, March.
    5. Rehim Kılıç, 2016. "Tests for Linearity in Star Models: Supwald and Lm-Type Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 660-674, September.
    6. Christian Francq & Baye Matar Kandji & Jean-Michel Zakoian, 2022. "Inference on Multiplicative Component GARCH without any Small-Order Moment," Working Papers 2022-09, Center for Research in Economics and Statistics.

  12. Christian FRANCQ & Lajos HORVATH & Jean-Michel ZAKOIAN, 2009. "Merits and Drawbacks of Variance Targeting in GARCH Models," Working Papers 2009-17, Center for Research in Economics and Statistics.

    Cited by:

    1. Rasmus Søndergaard Pedersen & Anders Rahbek, 2012. "Multivariate Variance Targeting in the BEKK-GARCH Model," Discussion Papers 12-23, University of Copenhagen. Department of Economics.
    2. Rasmus Søndergaard Pedersen, 2014. "Targeting estimation of CCC-Garch models with infinite fourth moments," Discussion Papers 14-04, University of Copenhagen. Department of Economics.
    3. Christian Francq & Lajos Horváth & Jean-Michel Zakoïan, 2016. "Variance Targeting Estimation of Multivariate GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 353-382.
    4. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    5. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    6. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    7. Esben Hedegaard & Robert J. Hodrick, 2014. "Estimating the Risk-Return Trade-off with Overlapping Data Inference," NBER Working Papers 19969, National Bureau of Economic Research, Inc.
    8. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    9. Hafner, Christian M. & Linton, Oliver, 2017. "An Almost Closed Form Estimator For The Egarch Model," Econometric Theory, Cambridge University Press, vol. 33(4), pages 1013-1038, August.
    10. Anatolyev Stanislav, 2019. "Volatility filtering in estimation of kurtosis (and variance)," Dependence Modeling, De Gruyter, vol. 7(1), pages 1-23, February.
    11. Ahmad, Wasim & Prakash, Ravi & Uddin, Gazi Salah & Chahal, Rishman Jot Kaur & Rahman, Md. Lutfur & Dutta, Anupam, 2020. "On the intraday dynamics of oil price and exchange rate: What can we learn from China and India?," Energy Economics, Elsevier, vol. 91(C).
    12. Francq, Christian & Sucarrat, Genaro, 2013. "An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation," MPRA Paper 51783, University Library of Munich, Germany.
    13. Todd, Prono, 2010. "Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 20034, University Library of Munich, Germany.
    14. Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI & Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI & Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," LIDAM Reprints CORE 2812, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Braione, Manuela & Scholtes, Nicolas K., 2014. "Construction of value-at-risk forecasts under different distributional assumptions within a BEKK framework," LIDAM Discussion Papers CORE 2014059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael & Pauwels, Laurent, 2019. "Asymptotic Theory for Rotated Multivariate GARCH Models," Working Papers BAWP-2019-03, University of Sydney Business School, Discipline of Business Analytics.
    17. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.
    18. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    19. Werge, Nicklas & Wintenberger, Olivier, 2022. "AdaVol: An Adaptive Recursive Volatility Prediction Method," Econometrics and Statistics, Elsevier, vol. 23(C), pages 19-35.
    20. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    21. Aielli, Gian Piero & Caporin, Massimiliano, 2013. "Fast clustering of GARCH processes via Gaussian mixture models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 205-222.
    22. Celso Brunetti & David Reiffen, 2011. "Commodity index trading and hedging costs," Finance and Economics Discussion Series 2011-57, Board of Governors of the Federal Reserve System (U.S.).
    23. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    24. Asai, Manabu, 2023. "Feasible Panel GARCH Models: Variance-Targeting Estimation and Empirical Application," Econometrics and Statistics, Elsevier, vol. 25(C), pages 23-38.
    25. Semeyutin, Artur & O’Neill, Robert, 2019. "A brief survey on the choice of parameters for: “Kernel density estimation for time series data”," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    26. Thieu, Le Quyen, 2016. "Variance targeting estimation of the BEKK-X model," MPRA Paper 75572, University Library of Munich, Germany.
    27. Luis García-Álvarez & Richard Luger, 2011. "Dynamic Correlations, Estimation Risk, and Porfolio Management During the Financial Crisis," Working Papers wp2011_1103, CEMFI, revised Sep 2011.
    28. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    29. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    30. Todd, Prono, 2009. "Simple, Skewness-Based GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 30994, University Library of Munich, Germany, revised 30 Jul 2011.
    31. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    32. Hafner, Christian M. & Reznikova, Olga, 2012. "On the estimation of dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3533-3545.
    33. Qi Li & Fukang Zhu, 2020. "Mean targeting estimator for the integer-valued GARCH(1, 1) model," Statistical Papers, Springer, vol. 61(2), pages 659-679, April.
    34. Stanislav Khrapov, 2011. "Pricing Central Tendency in Volatility," Working Papers w0168, Center for Economic and Financial Research (CEFIR).
    35. Guo, Zi-Yi, 2017. "Empirical Performance of GARCH Models with Heavy-tailed Innovations," EconStor Preprints 167626, ZBW - Leibniz Information Centre for Economics.
    36. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
    37. 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.
    38. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.
    39. Todd Prono, 2016. "Closed-Form Estimation of Finite-Order ARCH Models: Asymptotic Theory and Finite-Sample Performance," Finance and Economics Discussion Series 2016-083, Board of Governors of the Federal Reserve System (U.S.).
    40. Asai Manabu & So Mike K. P., 2023. "Realized BEKK-CAW Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 49-77, January.
    41. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    42. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    43. Hafner C. & Linton, O., 2013. "An Almost Closed Form Estimator for the EGARCH," LIDAM Discussion Papers ISBA 2013010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    44. Aue, Alexander & Horvath, Lajos & Pellatt, Daniel, 2015. "Functional generalized autoregressive conditional heteroskedasticity," MPRA Paper 67702, University Library of Munich, Germany.
    45. 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.
    46. Joseph de Vilmarest & Nicklas Werge, 2023. "An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition," Papers 2303.01855, arXiv.org, revised Jun 2024.

  13. HORVATH, Lajos & KOKOSZKA, Piotr & TEYSSIÈRE , Gilles, 2003. "Bootstrap misspecification tests for ARCH based on the empirical process of squared residuals," LIDAM Discussion Papers CORE 2003009, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Zhu, Ke, 2015. "Hausman tests for the error distribution in conditionally heteroskedastic models," MPRA Paper 66991, University Library of Munich, Germany.

  14. Horvath, L. & Kokoszka, P. & Teyssiere, G., 1999. "Empirical Process of the Squared Residuals of an ARCH Sequence," G.R.E.Q.A.M. 99a44, Universite Aix-Marseille III.

    Cited by:

    1. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 77-124.
    2. Zhu, Ke, 2015. "Hausman tests for the error distribution in conditionally heteroskedastic models," MPRA Paper 66991, University Library of Munich, Germany.
    3. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    4. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.

Articles

  1. Horváth, Lajos & Li, Hemei & Liu, Zhenya, 2022. "How to identify the different phases of stock market bubbles statistically?," Finance Research Letters, Elsevier, vol. 46(PA).
    See citations under working paper version above.
  2. 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).

    Cited by:

    1. Hu, Qirui, 2024. "Change point analysis of functional variance function with stationary error," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    2. Stergios B. Fotopoulos & Abhishek Kaul & Vasileios Pavlopoulos & Venkata K. Jandhyala, 2024. "Adaptive parametric change point inference under covariance structure changes," Statistical Papers, Springer, vol. 65(5), pages 2887-2913, July.

  3. Horváth, Lajos & Liu, Zhenya & Lu, Shanglin, 2022. "Sequential Monitoring Of Changes In Dynamic Linear Models, Applied To The U.S. Housing Market," Econometric Theory, Cambridge University Press, vol. 38(2), pages 209-272, April.
    See citations under working paper version above.
  4. Lajos Horváth & Curtis Miller & Gregory Rice, 2021. "Detecting early or late changes in linear models with heteroscedastic errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 577-609, June.

    Cited by:

    1. Natalie Neumeyer & Miguel A. Delgado & Lajos Horváth & Simos Meintanis & Emanuele Taufer & Lixing Zhu, 2021. "4th Workshop on Goodness‐of‐Fit, Change‐Point, and Related Problems, Trento, 2019," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 371-374, June.
    2. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.

  5. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.

    Cited by:

    1. Bruno Ebner & Norbert Henze, 2020. "Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 845-892, December.
    2. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).

  6. Horváth, Lajos & Li, Bo & Li, Hemei & Liu, Zhenya, 2020. "Time-varying beta in functional factor models: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

    Cited by:

    1. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Miralles-Quirós, José Luis & Miralles-Quirós, María Mar, 2024. "Factor models and investment strategies in the renewable energy sector," Energy Economics, Elsevier, vol. 132(C).
    3. Zhao, Yuqian, 2021. "Validating intra-day risk premium in cross-sectional return curves," Finance Research Letters, Elsevier, vol. 43(C).

  7. Lajos Horváth & Curtis Miller & Gregory Rice, 2020. "A New Class of Change Point Test Statistics of Rényi Type," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 570-579, July.

    Cited by:

    1. Lajos Horváth & Curtis Miller & Gregory Rice, 2021. "Detecting early or late changes in linear models with heteroscedastic errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 577-609, June.
    2. 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).
    3. David Ardia & Arnaud Dufays & Carlos Ordás Criado, 2024. "Linking Frequentist and Bayesian Change-Point Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1155-1168, October.
    4. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    5. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    6. 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.
    7. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

  8. 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.
    See citations under working paper version above.
  9. Horváth, Lajos & Kokoszka, Piotr & Wang, Shixuan, 2020. "Testing normality of data on a multivariate grid," Journal of Multivariate Analysis, Elsevier, vol. 179(C).

    Cited by:

    1. Wanfang Chen & Marc G. Genton, 2023. "Are You All Normal? It Depends!," International Statistical Review, International Statistical Institute, vol. 91(1), pages 114-139, April.

  10. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "Sequential monitoring for changes from stationarity to mild non-stationarity," Journal of Econometrics, Elsevier, vol. 215(1), pages 209-238.

    Cited by:

    1. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).
    2. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.
    3. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    4. Li, Hemei & Liu, Zhenya & Xiao, Zhijie, 2024. "Sequential monitoring of stock market price changes," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 156-172.
    5. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    6. Christis Katsouris, 2022. "Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models," Papers 2202.00141, arXiv.org, revised Feb 2022.
    7. 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).
    8. Lajos Horv'ath & Zhenya Liu & Shanglin Lu, 2020. "Sequential Monitoring of Changes in Housing Prices," Papers 2002.04101, arXiv.org.
    9. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    10. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.
    11. 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.
    12. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.

  11. Ruanmin Cao & Lajos Horváth & Zhenya Liu & Yuqian Zhao, 2020. "A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 335-358, January.
    See citations under working paper version above.
  12. Marco Barassi & Lajos Horváth & Yuqian Zhao, 2020. "Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 340-349, April.
    See citations under working paper version above.
  13. Horváth, Lajos & Trapani, Lorenzo, 2019. "Testing for randomness in a random coefficient autoregression model," Journal of Econometrics, Elsevier, vol. 209(2), pages 338-352.
    See citations under working paper version above.
  14. 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.

    Cited by:

    1. Steland, Ansgar, 2020. "Testing and estimating change-points in the covariance matrix of a high-dimensional time series," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    2. Patrice Abry & B. Cooper Boniece & Gustavo Didier & Herwig Wendt, 2023. "Wavelet eigenvalue regression in high dimensions," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 1-32, April.

  15. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    See citations under working paper version above.
  16. Górecki, Tomasz & Horváth, Lajos & Kokoszka, Piotr, 2018. "Change point detection in heteroscedastic time series," Econometrics and Statistics, Elsevier, vol. 7(C), pages 63-88.

    Cited by:

    1. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    2. Pierre Perron & Yohei Yamamoto, 2022. "Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 389-411, May.
    3. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.
    4. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    5. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
    6. Perron, Pierre & Yamamoto, Yohei & 山本, 庸平 & Zhou, Jing, 2019. "Testing Jointly for Structural Changes in the Error Variance and Coefficients of a Linear Regression Model," Discussion paper series HIAS-E-85, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    7. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    8. Michael Messer, 2022. "Bivariate change point detection: Joint detection of changes in expectation and variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 886-916, June.
    9. Muhammad Rizwan Khan & Biswajit Sarkar, 2019. "Change Point Detection for Airborne Particulate Matter ( PM 2.5 , PM 10 ) by Using the Bayesian Approach," Mathematics, MDPI, vol. 7(5), pages 1-42, May.
    10. 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.
    11. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
    12. Jialiang Li & Yaguang Li & Tailen Hsing, 2022. "On functional processes with multiple discontinuities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 933-972, July.

  17. Horváth, Lajos & Hušková, Marie & Rice, Gregory & Wang, Jia, 2017. "Asymptotic Properties Of The Cusum Estimator For The Time Of Change In Linear Panel Data Models," Econometric Theory, Cambridge University Press, vol. 33(2), pages 366-412, April.

    Cited by:

    1. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    2. 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.
    3. Westerlund, Joakim & Nordström, Marcus, 2021. "Breaks in persistence in fixed-T panel data," Economics Letters, Elsevier, vol. 205(C).

  18. Lajos Horváth & William Pouliot & Shixuan Wang, 2017. "Detecting at-Most-m Changes in Linear Regression Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 552-590, July.

    Cited by:

    1. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.
    2. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    3. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    4. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).

  19. Alexander Aue & Lajos Horváth & Daniel F. Pellatt, 2017. "Functional Generalized Autoregressive Conditional Heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 3-21, January.
    See citations under working paper version above.
  20. Patrick Bardsley & Lajos Horváth & Piotr Kokoszka & Gabriel Young, 2017. "Change point tests in functional factor models with application to yield curves," Econometrics Journal, Royal Economic Society, vol. 20(1), pages 86-117, February.

    Cited by:

    1. Lajos Horváth & Curtis Miller & Gregory Rice, 2021. "Detecting early or late changes in linear models with heteroscedastic errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 577-609, June.
    2. 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).
    3. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    4. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    5. Marc Hallin & Gilles Nisol & Shahin Tavakoli, 2023. "Factor models for high‐dimensional functional time series I: Representation results," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 578-600, September.
    6. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    7. Farzad Sabzikar & Piotr Kokoszka, 2023. "Tempered functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 280-293, May.
    8. Jialiang Li & Yaguang Li & Tailen Hsing, 2022. "On functional processes with multiple discontinuities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 933-972, July.

  21. Christian Francq & Lajos Horváth & Jean-Michel Zakoïan, 2016. "Variance Targeting Estimation of Multivariate GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 353-382.
    See citations under working paper version above.
  22. Horváth, Lajos & Trapani, Lorenzo, 2016. "Statistical inference in a random coefficient panel model," Journal of Econometrics, Elsevier, vol. 193(1), pages 54-75.

    Cited by:

    1. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2019. "Random coefficient continuous systems: Testing for extreme sample path behavior," Journal of Econometrics, Elsevier, vol. 209(2), pages 208-237.
    2. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    3. Trapani, Lorenzo, 2021. "A test for strict stationarity in a random coefficient autoregressive model of order 1," Statistics & Probability Letters, Elsevier, vol. 177(C).
    4. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    5. Degiannakis, Stavros & Filis, George & Siourounis, Grigorios & Trapani, Lorenzo, 2019. "Superkurtosis," MPRA Paper 96563, University Library of Munich, Germany.
      • Stavros Degiannakis & George Filis & Grigorios Siourounis & Lorenzo Trapani, 2023. "Superkurtosis," Working Papers 318, Bank of Greece.
      • Degiannakis, Stavros & Filis, George & Siourounis, Grigorios & Trapani, Lorenzo, 2019. "Superkurtosis," MPRA Paper 94473, University Library of Munich, Germany.
      • Stavros Degiannakis & George Filis & Grigorios Siourounis & Lorenzo Trapani, 2023. "Superkurtosis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2061-2091, December.
    6. Abonazel, Mohamed R., 2016. "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties," MPRA Paper 72586, University Library of Munich, Germany.
    7. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.
    8. Marie Badreau & Frédéric Proïa, 2023. "Consistency and asymptotic normality in a class of nearly unstable processes," Statistical Inference for Stochastic Processes, Springer, vol. 26(3), pages 619-641, October.
    9. Lajos Horvath & Lorenzo Trapani, 2018. "Testing for randomness in a random coefficient autoregression model," Discussion Papers 18/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    10. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    11. Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    12. Hwang, Eunju, 2021. "Weighted least squares estimation in a binary random coefficient panel model with infinite variance," Statistics & Probability Letters, Elsevier, vol. 168(C).
    13. Yuxin Pang & Dehui Wang, 2024. "A New Random Coefficient Autoregressive Model Driven by an Unobservable State Variable," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
    14. Breitung, Jörg & Salish, Nazarii, 2021. "Estimation of heterogeneous panels with systematic slope variations," Journal of Econometrics, Elsevier, vol. 220(2), pages 399-415.
    15. Zheqi Wang & Dehui Wang & Jianhua Cheng, 2023. "A new autoregressive process driven by explanatory variables and past observations: an application to PM 2.5," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 619-658, June.

  23. 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.

    Cited by:

    1. 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.
    2. Dimitrios Pilavakis & Efstathios Paparoditis & Theofanis Sapatinas, 2020. "Testing equality of autocovariance operators for functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 571-589, July.
    3. 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.
    4. Yang, Yang & Yang, Yanrong & Shang, Han Lin, 2022. "Feature extraction for functional time series: Theory and application to NIR spectroscopy data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    5. 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.

  24. Berkes, István & Horváth, Lajos & Rice, Gregory, 2016. "On the asymptotic normality of kernel estimators of the long run covariance of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 150-175.

    Cited by:

    1. 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).
    2. Gregory Rice & Han Lin Shang, 2017. "A Plug-in Bandwidth Selection Procedure for Long-Run Covariance Estimation with Stationary Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 591-609, July.
    3. Shang Han Lin, 2020. "A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-39, January.
    4. 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.
    5. 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.
    6. Rademacher, Daniel & Kreiß, Jens-Peter & Paparoditis, Efstathios, 2024. "Asymptotic normality of spectral means of Hilbert space valued random processes," Stochastic Processes and their Applications, Elsevier, vol. 173(C).
    7. Dimitrios Pilavakis & Efstathios Paparoditis & Theofanis Sapatinas, 2020. "Testing equality of autocovariance operators for functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 571-589, July.
    8. 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.
    9. Yang, Yang & Yang, Yanrong & Shang, Han Lin, 2022. "Feature extraction for functional time series: Theory and application to NIR spectroscopy data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. 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.
    11. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    12. 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.

  25. Horváth, Lajos & Rice, Gregory, 2015. "Testing for independence between functional time series," Journal of Econometrics, Elsevier, vol. 189(2), pages 371-382.

    Cited by:

    1. Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
    2. 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).
    3. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2021. "Testing serial independence with functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 603-629, September.
    4. Ling, S. & McAleer, M.J. & Tong, H., 2015. "Frontiers in Time Series and Financial Econometrics," Econometric Institute Research Papers EI 2015-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Manfoumbi Djonguet, Terence Kevin & Nkiet, Guy Martial, 2024. "An independence test for functional variables based on kernel normalized cross-covariance operator," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    6. Boudou, Alain & Viguier-Pla, Sylvie, 2016. "Gap between orthogonal projectors—Application to stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 282-300.
    7. Berkes, István & Horváth, Lajos & Rice, Gregory, 2016. "On the asymptotic normality of kernel estimators of the long run covariance of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 150-175.
    8. Shiqing Ling & Michael McAleer & Howell Tong, 2015. "Frontiers in Time Series and Financial Econometrics: An Overview," Documentos de Trabajo del ICAE 2015-04, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    9. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. Krzyśko Mirosław & Smaga Łukasz, 2020. "Measuring and Testing Mutual Dependence of Multivariate Functional Data," Statistics in Transition New Series, Statistics Poland, vol. 21(3), pages 21-37, September.

  26. Lajos Horváth & Gregory Rice, 2015. "Testing Equality Of Means When The Observations Are From Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 84-108, January.

    Cited by:

    1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Interactions between Eurozone and US Booms and Busts: A Bayesian Panel Markov-switching VAR Model," Tinbergen Institute Discussion Papers 13-142/III, Tinbergen Institute, revised 01 Nov 2014.
    2. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. Van Dijk, 2016. "Interconnections Between Eurozone and us Booms and Busts Using a Bayesian Panel Markov‐Switching VAR Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1352-1370, November.
    3. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    4. Zhang, Rongmao & Chan, Ngai Hang & Chi, Changxiong, 2023. "Nonparametric testing for the specification of spatial trend functions," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    5. Dimitrios Pilavakis & Efstathios Paparoditis & Theofanis Sapatinas, 2020. "Testing equality of autocovariance operators for functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 571-589, July.
    6. 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.
    7. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.

  27. Lajos Horváth & Gregory Rice, 2014. "Rejoinder on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 287-290, June.

    Cited by:

    1. Haeran Cho & Claudia Kirch, 2022. "Two-stage data segmentation permitting multiscale change points, heavy tails and dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 653-684, August.
    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. 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).
    4. Barassi, Marco & Horvath, Lajos & Zhao, Yuqian, 2018. "Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," MPRA Paper 87837, University Library of Munich, Germany.
    5. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "Sequential monitoring for changes from stationarity to mild non-stationarity," Journal of Econometrics, Elsevier, vol. 215(1), pages 209-238.
    6. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    7. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
    8. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
    9. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    10. Claudia Kirch & Christina Stoehr, 2022. "Sequential change point tests based on U‐statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1184-1214, September.
    11. Bertille Follain & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional changepoint estimation with heterogeneous missingness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 1023-1055, July.
    12. Tianming Xu & Yuesong Wei, 2023. "Ratio Test for Mean Changes in Time Series with Heavy-Tailed AR( p ) Noise Based on Multiple Sampling Methods," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    13. Follain, Bertille & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional changepoint estimation with heterogeneous missingness," LSE Research Online Documents on Economics 115014, London School of Economics and Political Science, LSE Library.
    14. Christian Kleiber, 2017. "Structural Change in (Economic) Time Series," Papers 1702.06913, arXiv.org.
    15. Tianming Xu & Dong Jiang & Yuesong Wei & Chong Wang, 2025. "A Test for Trend Gradual Changes in Heavy Tailed AR (p) Sequences," Statistical Papers, Springer, vol. 66(1), pages 1-20, February.
    16. Liu, Bin & Zhang, Xinsheng & Liu, Yufeng, 2022. "High dimensional change point inference: Recent developments and extensions," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    17. Changryong Baek & Piotr Kokoszka & Xiangdong Meng, 2024. "Test of change point versus long‐range dependence in functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 497-512, July.
    18. 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.
    19. Sergio Alvarez-Andrade & Salim Bouzebda & Aimé Lachal, 2018. "Strong approximations for the p-fold integrated empirical process with applications to statistical tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 826-849, December.
    20. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
    21. 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).
    22. Saisai Ding & Xiaoqin Li & Xiang Dong & Wenzhi Yang, 2020. "The Consistency of the CUSUM-Type Estimator of the Change-Point and Its Application," Mathematics, MDPI, vol. 8(12), pages 1-12, November.
    23. Bouzebda, Salim & Ferfache, Anouar Abdeldjaoued, 2023. "Asymptotic properties of semiparametric M-estimators with multiple change points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    24. Stergios B. Fotopoulos & Abhishek Kaul & Vasileios Pavlopoulos & Venkata K. Jandhyala, 2024. "Adaptive parametric change point inference under covariance structure changes," Statistical Papers, Springer, vol. 65(5), pages 2887-2913, July.
    25. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    26. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    27. Yudong Chen & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional, multiscale online changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 234-266, February.
    28. Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
    29. Lorenzo Escot & Julio E. Sandubete & Łukasz Pietrych, 2023. "Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets," Mathematics, MDPI, vol. 11(23), pages 1-18, December.
    30. Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
    31. Hahn, Georg, 2022. "Online multivariate changepoint detection with type I error control and constant time/memory updates per series," Statistics & Probability Letters, Elsevier, vol. 181(C).
    32. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    33. Yi Wu & Wei Wang & Xuejun Wang, 2024. "Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients," Computational Statistics, Springer, vol. 39(7), pages 3753-3778, December.

  28. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.

    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. 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.
    3. 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.
    4. 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.
    5. Alessandro Casini & Pierre Perron, 2021. "Change-Point Analysis of Time Series with Evolutionary Spectra," Papers 2106.02031, arXiv.org, revised Aug 2024.
    6. 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).
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. Won‐Ki Seo, 2024. "Functional principal component analysis for cointegrated functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(2), pages 320-330, March.
    12. 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.
    13. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Cerovecki, Clément & Francq, Christian & Hormann, Siegfried & Zakoian, Jean-Michel, 2018. "Functional GARCH models: the quasi-likelihood approach and its applications," MPRA Paper 83990, University Library of Munich, Germany.
    23. 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.
    24. 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.
    25. 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).
    26. 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.
    27. 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.
    28. Han Lin Shang, 2024. "Bootstrapping Long-Run Covariance of Stationary Functional Time Series," Forecasting, MDPI, vol. 6(1), pages 1-14, February.
    29. 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.
    30. 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.
    31. 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.
    32. 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).
    33. Changryong Baek & Piotr Kokoszka & Xiangdong Meng, 2024. "Test of change point versus long‐range dependence in functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 497-512, July.
    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. 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.
    36. 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.
    37. 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).
    38. 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.
    39. 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.
    40. 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.
    41. Haixu Wang & Jiguo Cao, 2023. "Nonlinear prediction of functional time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    42. Oomen, Roel, 2018. "Price signatures," LSE Research Online Documents on Economics 90481, London School of Economics and Political Science, LSE Library.
    43. Farzad Sabzikar & Piotr Kokoszka, 2023. "Tempered functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 280-293, May.
    44. 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.
    45. Holger Dette & Pascal Quanz, 2023. "Detecting relevant changes in the spatiotemporal mean function," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 505-532, September.
    46. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01821815, HAL.
    47. Horváth, Lajos & Rice, Gregory, 2015. "Testing for independence between functional time series," Journal of Econometrics, Elsevier, vol. 189(2), pages 371-382.
    48. 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.
    49. 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).
    50. 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.
    51. 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).
    52. B. Li & S. Boubaker & Z. Liu & W. Louhichi & Y. Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Post-Print hal-04435519, HAL.
    53. 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.
    54. 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.
    55. Matteo Iacopini & Dominique Guégan, 2018. "Nonparametric Forecasting of Multivariate Probability Density Functions," Working Papers 2018:15, Department of Economics, University of Venice "Ca' Foscari".
    56. 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.
    57. 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.
    58. Li, Xuemei & Liu, Xiaoxing, 2023. "Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures," Energy, Elsevier, vol. 284(C).
    59. Zhao, Yuqian, 2021. "Validating intra-day risk premium in cross-sectional return curves," Finance Research Letters, Elsevier, vol. 43(C).
    60. 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.
    61. 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.
    62. 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.
    63. 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.
    64. 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.
    65. 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.
    66. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Post-Print halshs-01821815, HAL.

  29. Aue, Alexander & Horváth, Lajos & Hurvich, Clifford & Soulier, Philippe, 2014. "Limit Laws In Transaction-Level Asset Price Models," Econometric Theory, Cambridge University Press, vol. 30(3), pages 536-579, June.
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  30. Fremdt, Stefan & Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef G., 2014. "Functional data analysis with increasing number of projections," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 313-332.

    Cited by:

    1. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    2. Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Balogoun, Armando Sosthène Kali & Nkiet, Guy Martial & Ogouyandjou, Carlos, 2021. "Asymptotic normality of a generalized maximum mean discrepancy estimator," Statistics & Probability Letters, Elsevier, vol. 169(C).
    4. Leonid Torgovitski, 2015. "A Darling–Erdős-type CUSUM-procedure for functional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(1), pages 1-27, January.
    5. 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.
    6. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.
    7. R. Bárcenas & J. Ortega & A. J. Quiroz, 2017. "Quadratic forms of the empirical processes for the two-sample problem for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 503-526, September.
    8. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    9. T. Górecki & Ł. Smaga, 2017. "Multivariate analysis of variance for functional data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2172-2189, September.
    10. Manuel Febrero-Bande & Pedro Galeano & Wenceslao González-Manteiga, 2017. "Functional Principal Component Regression and Functional Partial Least-squares Regression: An Overview and a Comparative Study," International Statistical Review, International Statistical Institute, vol. 85(1), pages 61-83, April.

  31. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.

    Cited by:

    1. Haeran Cho & Claudia Kirch, 2022. "Two-stage data segmentation permitting multiscale change points, heavy tails and dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 653-684, August.
    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. 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).
    4. Barassi, Marco & Horvath, Lajos & Zhao, Yuqian, 2018. "Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," MPRA Paper 87837, University Library of Munich, Germany.
    5. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "Sequential monitoring for changes from stationarity to mild non-stationarity," Journal of Econometrics, Elsevier, vol. 215(1), pages 209-238.
    6. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    7. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
    8. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
    9. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    10. Claudia Kirch & Christina Stoehr, 2022. "Sequential change point tests based on U‐statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1184-1214, September.
    11. Bertille Follain & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional changepoint estimation with heterogeneous missingness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 1023-1055, July.
    12. Tianming Xu & Yuesong Wei, 2023. "Ratio Test for Mean Changes in Time Series with Heavy-Tailed AR( p ) Noise Based on Multiple Sampling Methods," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    13. Follain, Bertille & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional changepoint estimation with heterogeneous missingness," LSE Research Online Documents on Economics 115014, London School of Economics and Political Science, LSE Library.
    14. Christian Kleiber, 2017. "Structural Change in (Economic) Time Series," Papers 1702.06913, arXiv.org.
    15. Tianming Xu & Dong Jiang & Yuesong Wei & Chong Wang, 2025. "A Test for Trend Gradual Changes in Heavy Tailed AR (p) Sequences," Statistical Papers, Springer, vol. 66(1), pages 1-20, February.
    16. Liu, Bin & Zhang, Xinsheng & Liu, Yufeng, 2022. "High dimensional change point inference: Recent developments and extensions," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    17. Changryong Baek & Piotr Kokoszka & Xiangdong Meng, 2024. "Test of change point versus long‐range dependence in functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 497-512, July.
    18. 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.
    19. Sergio Alvarez-Andrade & Salim Bouzebda & Aimé Lachal, 2018. "Strong approximations for the p-fold integrated empirical process with applications to statistical tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 826-849, December.
    20. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
    21. 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).
    22. Saisai Ding & Xiaoqin Li & Xiang Dong & Wenzhi Yang, 2020. "The Consistency of the CUSUM-Type Estimator of the Change-Point and Its Application," Mathematics, MDPI, vol. 8(12), pages 1-12, November.
    23. Bouzebda, Salim & Ferfache, Anouar Abdeldjaoued, 2023. "Asymptotic properties of semiparametric M-estimators with multiple change points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    24. Stergios B. Fotopoulos & Abhishek Kaul & Vasileios Pavlopoulos & Venkata K. Jandhyala, 2024. "Adaptive parametric change point inference under covariance structure changes," Statistical Papers, Springer, vol. 65(5), pages 2887-2913, July.
    25. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    26. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    27. Yudong Chen & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional, multiscale online changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 234-266, February.
    28. Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
    29. Lorenzo Escot & Julio E. Sandubete & Łukasz Pietrych, 2023. "Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets," Mathematics, MDPI, vol. 11(23), pages 1-18, December.
    30. Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
    31. Hahn, Georg, 2022. "Online multivariate changepoint detection with type I error control and constant time/memory updates per series," Statistics & Probability Letters, Elsevier, vol. 181(C).
    32. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    33. Yi Wu & Wei Wang & Xuejun Wang, 2024. "Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients," Computational Statistics, Springer, vol. 39(7), pages 3753-3778, December.

  32. Batsidis, A. & Horváth, L. & Martín, N. & Pardo, L. & Zografos, K., 2013. "Change-point detection in multinomial data using phi-divergence test statistics," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 53-66.

    Cited by:

    1. Nirian Martín & Leandro Pardo, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 279-282, June.
    2. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    3. Byungsoo Kim & Junmo Song & Changryong Baek, 2021. "Robust test for structural instability in dynamic factor models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 821-853, August.

  33. Horváth, Lajos & Hušková, Marie & Rice, Gregory, 2013. "Test of independence for functional data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 100-119.

    Cited by:

    1. 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.
    2. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    3. 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.
    4. 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.
    5. Mestre, Guillermo & Portela, José & Rice, Gregory & Muñoz San Roque, Antonio & Alonso, Estrella, 2021. "Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    6. Hui Ding & Mei Yao & Riquan Zhang, 2023. "A new estimation in functional linear concurrent model with covariate dependent and noise contamination," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(8), pages 965-989, November.
    7. Atefeh Zamani & Hossein Haghbin & Maryam Hashemi & Rob J Hyndman, 2019. "Seasonal Functional Autoregressive Models," Monash Econometrics and Business Statistics Working Papers 16/19, Monash University, Department of Econometrics and Business Statistics.
    8. Valentina Masarotto & Victor M. Panaretos & Yoav Zemel, 2019. "Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 172-213, February.
    9. 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).
    10. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2021. "Testing serial independence with functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 603-629, September.
    11. 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.
    12. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.
    13. 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.
    14. Petrovich, Justin & Reimherr, Matthew, 2017. "Asymptotic properties of principal component projections with repeated eigenvalues," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 42-48.
    15. Horváth, Lajos & Rice, Gregory, 2015. "Testing for independence between functional time series," Journal of Econometrics, Elsevier, vol. 189(2), pages 371-382.
    16. Petr Čoupek & Viktor Dolník & Zdeněk Hlávka & Daniel Hlubinka, 2024. "Fourier approach to goodness-of-fit tests for Gaussian random processes," Statistical Papers, Springer, vol. 65(5), pages 2937-2972, July.

  34. Lajos Horváth & Piotr Kokoszka & Ron Reeder, 2013. "Estimation of the mean of functional time series and a two-sample problem," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 103-122, January.

    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. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    3. Panaretos, Victor M. & Tavakoli, Shahin, 2013. "Cramér–Karhunen–Loève representation and harmonic principal component analysis of functional time series," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2779-2807.
    4. Ayyala, Deepak Nag & Park, Junyong & Roy, Anindya, 2017. "Mean vector testing for high-dimensional dependent observations," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 136-155.
    5. Won‐Ki Seo, 2024. "Functional principal component analysis for cointegrated functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(2), pages 320-330, March.
    6. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    7. 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.
    8. 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.
    9. Atefeh Zamani & Hossein Haghbin & Maryam Hashemi & Rob J Hyndman, 2019. "Seasonal Functional Autoregressive Models," Monash Econometrics and Business Statistics Working Papers 16/19, Monash University, Department of Econometrics and Business Statistics.
    10. Kokoszka, Piotr & Reimherr, Matthew, 2013. "Asymptotic normality of the principal components of functional time series," Stochastic Processes and their Applications, Elsevier, vol. 123(5), pages 1546-1562.
    11. Shang Han Lin, 2020. "A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-39, January.
    12. Burdejova, Petra & Härdle, Wolfgang Karl & Kokoszka, Piotr & Xiong, Q., 2015. "Change point and trend analyses of annual expectile curves of tropical storms," SFB 649 Discussion Papers 2015-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Alexander S. Long & Brian J. Reich & Ana‐Maria Staicu & John Meitzen, 2023. "A nonparametric test of group distributional differences for hierarchically clustered functional data," Biometrics, The International Biometric Society, vol. 79(4), pages 3778-3791, December.
    14. Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    15. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    16. 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.
    17. Balogoun, Armando Sosthène Kali & Nkiet, Guy Martial & Ogouyandjou, Carlos, 2021. "Asymptotic normality of a generalized maximum mean discrepancy estimator," Statistics & Probability Letters, Elsevier, vol. 169(C).
    18. 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.
    19. Reimherr, Matthew, 2015. "Functional regression with repeated eigenvalues," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 62-70.
    20. 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.
    21. Horváth, Lajos & Hušková, Marie & Rice, Gregory, 2013. "Test of independence for functional data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 100-119.
    22. Leucht, Anne & Paparoditis, Efstathios & Rademacher, Daniel & Sapatinas, Theofanis, 2022. "Testing equality of spectral density operators for functional processes," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    23. Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2018. "Hotelling’s T2 in separable Hilbert spaces," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 284-305.
    24. 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.
    25. Haixu Wang & Jiguo Cao, 2023. "Nonlinear prediction of functional time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    26. R. Bárcenas & J. Ortega & A. J. Quiroz, 2017. "Quadratic forms of the empirical processes for the two-sample problem for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 503-526, September.
    27. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    28. Farzad Sabzikar & Piotr Kokoszka, 2023. "Tempered functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 280-293, May.
    29. 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.
    30. Berkes, István & Horváth, Lajos & Rice, Gregory, 2016. "On the asymptotic normality of kernel estimators of the long run covariance of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 150-175.
    31. Wang, Jiangyan & Cao, Guanqun & Wang, Li & Yang, Lijian, 2020. "Simultaneous confidence band for stationary covariance function of dense functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    32. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    33. B. Li & S. Boubaker & Z. Liu & W. Louhichi & Y. Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Post-Print hal-04435519, HAL.
    34. 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.
    35. Laya Ghodrati & Victor M. Panaretos, 2024. "On distributional autoregression and iterated transportation," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(5), pages 739-770, September.
    36. Liebl, Dominik, 2019. "Inference for sparse and dense functional data with covariate adjustments," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 315-335.
    37. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    38. Fremdt, Stefan & Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef G., 2014. "Functional data analysis with increasing number of projections," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 313-332.
    39. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    40. Qiu, Zhiping & Fan, Jiangyuan & Zhang, Jin-Ting & Chen, Jianwei, 2024. "Tests for equality of several covariance matrix functions for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    41. 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.
    42. Smida, Zaineb & Laurent, Thibault & Cucala, Lionel, 2024. "A Hotelling spatial scan statistic for functional data: application to economic and climate data," TSE Working Papers 24-1583, Toulouse School of Economics (TSE).

  35. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.

    Cited by:

    1. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    2. Michael Messer & Stefan Albert & Gaby Schneider, 2018. "The multiple filter test for change point detection in time series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 589-607, August.
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    Cited by:

    1. 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.
    2. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    3. 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.
    4. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    5. 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.
    6. Mestre, Guillermo & Portela, José & Rice, Gregory & Muñoz San Roque, Antonio & Alonso, Estrella, 2021. "Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    7. Atefeh Zamani & Hossein Haghbin & Maryam Hashemi & Rob J Hyndman, 2019. "Seasonal Functional Autoregressive Models," Monash Econometrics and Business Statistics Working Papers 16/19, Monash University, Department of Econometrics and Business Statistics.
    8. Francisco Martínez-Álvarez & Amandine Schmutz & Gualberto Asencio-Cortés & Julien Jacques, 2018. "A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand," Energies, MDPI, vol. 12(1), pages 1-18, December.
    9. 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.
    10. Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    11. Cerovecki, Clément & Francq, Christian & Hormann, Siegfried & Zakoian, Jean-Michel, 2018. "Functional GARCH models: the quasi-likelihood approach and its applications," MPRA Paper 83990, University Library of Munich, Germany.
    12. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2021. "Testing serial independence with functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 603-629, September.
    13. 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.
    14. Boukhiar, Souad & Mourid, Tahar, 2022. "Resolvent estimators for functional autoregressive processes with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    15. 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.
    16. Massimo Franchi & Paolo Paruolo, 2017. "Cointegration in functional autoregressive processes," DSS Empirical Economics and Econometrics Working Papers Series 2017/5, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    17. Fanyu Meng & Wenwu Gong & Jun Liang & Xian Li & Yiping Zeng & Lili Yang, 2021. "Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
    18. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    19. 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.
    20. Jiang Du & Hui Zhao & Zhongzhan Zhang, 2019. "Dynamic partially functional linear regression model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 679-693, December.
    21. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    22. Berkes, István & Horváth, Lajos & Rice, Gregory, 2016. "On the asymptotic normality of kernel estimators of the long run covariance of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 150-175.
    23. Baye Matar Kandji, 2023. "On the growth rate of superadditive processes and the stability of functional GARCH models," Working Papers 2023-07, Center for Research in Economics and Statistics.
    24. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    25. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
    26. 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).
    27. Aue, Alexander & Horvath, Lajos & Pellatt, Daniel, 2015. "Functional generalized autoregressive conditional heteroskedasticity," MPRA Paper 67702, University Library of Munich, Germany.
    28. Gao, Yuan & Shang, Han Lin & Yang, Yanrong, 2019. "High-dimensional functional time series forecasting: An application to age-specific mortality rates," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 232-243.
    29. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    30. van Delft, Anne & Eichler, Michael, 2017. "Locally Stationary Functional Time Series," LIDAM Discussion Papers ISBA 2017023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  37. Stefan Fremdt & Josef G. Steinebach & Lajos Horváth & Piotr Kokoszka, 2013. "Testing the Equality of Covariance Operators in Functional Samples," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(1), pages 138-152, March.

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    1. Ahmad, Rauf, 2022. "Tests for proportionality of matrices with large dimension," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Boente, Graciela & Rodriguez, Daniela & Sued, Mariela, 2019. "The spatial sign covariance operator: Asymptotic results and applications," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 115-128.
    3. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    4. Guo, Jia & Zhou, Bu & Zhang, Jin-Ting, 2018. "Testing the equality of several covariance functions for functional data: A supremum-norm based test," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 15-26.
    5. Kathrin Bissantz & Nicolai Bissantz & Katharina Proksch, 2021. "Nonparametric detection of changes over time in image data from fluorescence microscopy of living cells," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1001-1017, September.
    6. Holger Dette & Kevin Kokot, 2022. "Detecting relevant differences in the covariance operators of functional time series: a sup-norm approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 195-231, April.
    7. Alexander S. Long & Brian J. Reich & Ana‐Maria Staicu & John Meitzen, 2023. "A nonparametric test of group distributional differences for hierarchically clustered functional data," Biometrics, The International Biometric Society, vol. 79(4), pages 3778-3791, December.
    8. Ghiglietti, Andrea & Paganoni, Anna Maria, 2017. "Exact tests for the means of Gaussian stochastic processes," Statistics & Probability Letters, Elsevier, vol. 131(C), pages 102-107.
    9. Valentina Masarotto & Guido Masarotto, 2024. "Covariance‐based soft clustering of functional data based on the Wasserstein–Procrustes metric," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 485-512, June.
    10. Valentina Masarotto & Victor M. Panaretos & Yoav Zemel, 2019. "Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 172-213, February.
    11. Adam B. Kashlak & John A. D. Aston & Richard Nickl, 2019. "Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 214-243, February.
    12. 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.
    13. Dimitrios Pilavakis & Efstathios Paparoditis & Theofanis Sapatinas, 2020. "Testing equality of autocovariance operators for functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 571-589, July.
    14. Tao Zhang & Zhiwen Wang & Yanling Wan, 2021. "Functional test for high-dimensional covariance matrix, with application to mitochondrial calcium concentration," Statistical Papers, Springer, vol. 62(3), pages 1213-1230, June.
    15. 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.
    16. Horváth, Lajos & Hušková, Marie & Rice, Gregory, 2013. "Test of independence for functional data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 100-119.
    17. Graciela Boente & Daniela Rodriguez & Mariela Sued, 2018. "Testing equality between several populations covariance operators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 919-950, August.
    18. R. Bárcenas & J. Ortega & A. J. Quiroz, 2017. "Quadratic forms of the empirical processes for the two-sample problem for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 503-526, September.
    19. Zhang, Jin-Ting & Cheng, Ming-Yen & Wu, Hau-Tieng & Zhou, Bu, 2019. "A new test for functional one-way ANOVA with applications to ischemic heart screening," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 3-17.
    20. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    21. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    22. Jia Guo & Bu Zhou & Jianwei Chen & Jin-Ting Zhang, 2019. "An $${{\varvec{L}}}^{2}$$L2-norm-based test for equality of several covariance functions: a further study," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1092-1112, December.
    23. Hà Quang Minh, 2023. "Entropic Regularization of Wasserstein Distance Between Infinite-Dimensional Gaussian Measures and Gaussian Processes," Journal of Theoretical Probability, Springer, vol. 36(1), pages 201-296, March.
    24. Qiu, Zhiping & Fan, Jiangyuan & Zhang, Jin-Ting & Chen, Jianwei, 2024. "Tests for equality of several covariance matrix functions for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    25. González–Rodríguez, Gil & Colubi, Ana & González–Manteiga, Wenceslao & Febrero–Bande, Manuel, 2024. "A consistent test of equality of distributions for Hilbert-valued random elements," Journal of Multivariate Analysis, Elsevier, vol. 202(C).

  38. Berkes, István & Horváth, Lajos & Rice, Gregory, 2013. "Weak invariance principles for sums of dependent random functions," Stochastic Processes and their Applications, Elsevier, vol. 123(2), pages 385-403.

    Cited by:

    1. 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).
    2. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    3. Dehling, Herold & Sharipov, Olimjon Sh. & Wendler, Martin, 2015. "Bootstrap for dependent Hilbert space-valued random variables with application to von Mises statistics," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 200-215.
    4. 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.
    5. Burdejova, Petra & Härdle, Wolfgang Karl & Kokoszka, Piotr & Xiong, Q., 2015. "Change point and trend analyses of annual expectile curves of tropical storms," SFB 649 Discussion Papers 2015-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. 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.
    7. Lajos Horváth & Gregory Rice, 2015. "Testing Equality Of Means When The Observations Are From Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 84-108, January.
    8. Horváth, Lajos & Hušková, Marie & Rice, Gregory, 2013. "Test of independence for functional data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 100-119.
    9. 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.
    10. Górecki, Tomasz & Horváth, Lajos & Kokoszka, Piotr, 2018. "Change point detection in heteroscedastic time series," Econometrics and Statistics, Elsevier, vol. 7(C), pages 63-88.
    11. Berkes, István & Horváth, Lajos & Rice, Gregory, 2016. "On the asymptotic normality of kernel estimators of the long run covariance of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 150-175.
    12. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    13. Horváth, Lajos & Rice, Gregory, 2015. "Testing for independence between functional time series," Journal of Econometrics, Elsevier, vol. 189(2), pages 371-382.
    14. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    15. 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.

  39. Horváth, Lajos & Reeder, Ron, 2012. "Detecting changes in functional linear models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 310-334.

    Cited by:

    1. Aldo Goia & Philippe Vieu, 2015. "A partitioned Single Functional Index Model," Computational Statistics, Springer, vol. 30(3), pages 673-692, September.
    2. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    3. Algimantas Birbilas & Alfredas Račkauskas, 2024. "Change-Point Detection in Functional First-Order Auto-Regressive Models," Mathematics, MDPI, vol. 12(12), pages 1-25, June.

  40. Lajos Horváth & Marie Hušková, 2012. "Change-point detection in panel data," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(4), pages 631-648, July.

    Cited by:

    1. V. Brault & C. Lévy-Leduc & A. Mathieu & A. Jullien, 2018. "Change-Point Estimation in the Multivariate Model Taking into Account the Dependence: Application to the Vegetative Development of Oilseed Rape," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 374-389, September.
    2. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    3. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    4. Wenbiao Zhao & Lixing Zhu & Falong Tan, 2024. "Multiple change point detection for high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 809-846, September.

  41. Aue, Alexander & Hörmann, Siegfried & Horváth, Lajos & Hušková, Marie & Steinebach, Josef G., 2012. "Sequential Testing For The Stability Of High-Frequency Portfolio Betas," Econometric Theory, Cambridge University Press, vol. 28(4), pages 804-837, August.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Knorre, Fabian & Wagner, Martin & Grupe, Maximilian, 2020. "Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions," IHS Working Paper Series 27, Institute for Advanced Studies.
    3. Berkes, István & Horváth, Lajos & Rice, Gregory, 2013. "Weak invariance principles for sums of dependent random functions," Stochastic Processes and their Applications, Elsevier, vol. 123(2), pages 385-403.
    4. Martin Wagner & Dominik Wied, 2017. "Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 960-980, November.
    5. Reiß, Markus & Todorov, Viktor & Tauchen, George, 2014. "Nonparametric test for a constant beta over a fixed time interval," SFB 649 Discussion Papers 2014-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    7. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    8. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Reiß, Markus & Todorov, Viktor & Tauchen, George, 2015. "Nonparametric test for a constant beta between Itô semi-martingales based on high-frequency data," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2955-2988.
    10. Stoehr, Christina & Aston, John A D & Kirch, Claudia, 2021. "Detecting changes in the covariance structure of functional time series with application to fMRI data," Econometrics and Statistics, Elsevier, vol. 18(C), pages 44-62.
    11. Chochola, Ondřej & Hušková, Marie & Prášková, Zuzana & Steinebach, Josef G., 2013. "Robust monitoring of CAPM portfolio betas," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 374-395.
    12. Lajos Horváth & Gregory Rice, 2015. "Testing Equality Of Means When The Observations Are From Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 84-108, January.
    13. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    14. Chochola, Ondřej & Hušková, Marie & Prášková, Zuzana & Steinebach, Josef G., 2014. "Robust monitoring of CAPM portfolio betas II," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 58-81.
    15. Mikkel Bennedsen, 2021. "Designing a statistical procedure for monitoring global carbon dioxide emissions," Climatic Change, Springer, vol. 166(3), pages 1-19, June.

  42. Berkes, István & Horváth, Lajos, 2012. "The central limit theorem for sums of trimmed variables with heavy tails," Stochastic Processes and their Applications, Elsevier, vol. 122(2), pages 449-465.

    Cited by:

    1. Boubaker, Sabri & Liu, Zhenya & Sui, Tianqing & Zhai, Ling, 2022. "The mirror of history: How to statistically identify stock market bubble bursts," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 128-147.
    2. Lajos Horváth & Gregory Rice, 2014. "Rejoinder on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 287-290, June.
    3. Yuguang Fan, 2017. "Tightness and Convergence of Trimmed Lévy Processes to Normality at Small Times," Journal of Theoretical Probability, Springer, vol. 30(2), pages 675-699, June.
    4. Bazarova, Alina & Berkes, István & Horváth, Lajos, 2014. "On the central limit theorem for modulus trimmed sums," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 61-67.

  43. Aue, Alexander & Horváth, Lajos & Hušková, Marie, 2012. "Segmenting mean-nonstationary time series via trending regressions," Journal of Econometrics, Elsevier, vol. 168(2), pages 367-381.

    Cited by:

    1. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2016. "Testing for Instability in Covariance Structures," Working papers 2016-33, University of Connecticut, Department of Economics.
    2. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    3. Maria Mohr & Leonie Selk, 2020. "Estimating change points in nonparametric time series regression models," Statistical Papers, Springer, vol. 61(4), pages 1437-1463, August.

  44. Christian Francq & Lajos Horváth, 2011. "Merits and Drawbacks of Variance Targeting in GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(4), pages 619-656.
    See citations under working paper version above.
  45. István Berkes & Lajos Horváth & Shiqing Ling & Johannes Schauer, 2011. "Testing for structural change of AR model to threshold AR model," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 547-565, September.

    Cited by:

    1. Fukang Zhu & Mengya Liu & Shiqing Ling & Zongwu Cai, 2020. "Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202021, University of Kansas, Department of Economics, revised Dec 2020.
    2. Chong, Terence Tai Leung & Pang, Tianxiao & Zhang, Danna & Liang, Yanling, 2017. "Structural change in non-stationary AR(1) models," MPRA Paper 80510, University Library of Munich, Germany.

  46. Horváth, Lajos & Husková, Marie & Kokoszka, Piotr, 2010. "Testing the stability of the functional autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 352-367, February.

    Cited by:

    1. Axel Bücher & Holger Dette & Florian Heinrichs, 2020. "Detecting deviations from second-order stationarity in locally stationary functional time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 1055-1094, August.
    2. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
    3. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    4. Atefeh Zamani & Hossein Haghbin & Maryam Hashemi & Rob J Hyndman, 2019. "Seasonal Functional Autoregressive Models," Monash Econometrics and Business Statistics Working Papers 16/19, Monash University, Department of Econometrics and Business Statistics.
    5. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    6. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    7. Devin Didericksen & Piotr Kokoszka & Xi Zhang, 2012. "Empirical properties of forecasts with the functional autoregressive model," Computational Statistics, Springer, vol. 27(2), pages 285-298, June.
    8. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    9. A. Soltani & M. Hashemi, 2011. "Periodically correlated autoregressive Hilbertian processes," Statistical Inference for Stochastic Processes, Springer, vol. 14(2), pages 177-188, May.
    10. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    11. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    12. 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.
    13. Horváth, Lajos & Reeder, Ron, 2012. "Detecting changes in functional linear models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 310-334.
    14. van Delft, Anne & Eichler, Michael, 2017. "Locally Stationary Functional Time Series," LIDAM Discussion Papers ISBA 2017023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Zhou, Jie, 2011. "Maximum likelihood ratio test for the stability of sequence of Gaussian random processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2114-2127, June.

  47. Francq, Christian & Horvath, Lajos & Zakoïan, Jean-Michel, 2010. "Sup-Tests For Linearity In A General Nonlinear Ar(1) Model," Econometric Theory, Cambridge University Press, vol. 26(4), pages 965-993, August.
    See citations under working paper version above.
  48. Aue, Alexander & Horváth, Lajos & Hušková, Marie & Ling, Shiqing, 2009. "On Distinguishing Between Random Walk And Change In The Mean Alternatives," Econometric Theory, Cambridge University Press, vol. 25(2), pages 411-441, April.

    Cited by:

    1. 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).
    2. Holger Dette & Dominik Wied, 2016. "Detecting relevant changes in time series models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 371-394, March.
    3. Marco R. Barassi & Nicola Spagnolo & Yuqian Zhao, 2018. "Fractional Integration Versus Structural Change: Testing the Convergence of $$\hbox {CO}_{2}$$ CO 2 Emissions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(4), pages 923-968, December.

  49. István Berkes & Robertas Gabrys & Lajos Horváth & Piotr Kokoszka, 2009. "Detecting changes in the mean of functional observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 927-946, November.

    Cited by:

    1. Axel Bücher & Holger Dette & Florian Heinrichs, 2020. "Detecting deviations from second-order stationarity in locally stationary functional time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 1055-1094, August.
    2. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
    3. 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).
    4. Cai, Leheng & Hu, Qirui, 2024. "Simultaneous inference and uniform test for eigensystems of functional data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    5. 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.
    6. Oleksandr Gromenko & Piotr Kokoszka & Matthew Reimherr, 2017. "Detection of change in the spatiotemporal mean function," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 29-50, January.
    7. 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.
    8. Laha, A. K. & Rathi, Poonam, 2017. "Are the temperature of Indian cities Increasing?: Some Insights Using Change Point Analysis with Functional Data," IIMA Working Papers WP 2017-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Burdejova, Petra & Härdle, Wolfgang Karl & Kokoszka, Piotr & Xiong, Q., 2015. "Change point and trend analyses of annual expectile curves of tropical storms," SFB 649 Discussion Papers 2015-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. 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.
    11. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    12. Stoehr, Christina & Aston, John A D & Kirch, Claudia, 2021. "Detecting changes in the covariance structure of functional time series with application to fMRI data," Econometrics and Statistics, Elsevier, vol. 18(C), pages 44-62.
    13. 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).
    14. Leonid Torgovitski, 2015. "A Darling–Erdős-type CUSUM-procedure for functional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(1), pages 1-27, January.
    15. Changryong Baek & Piotr Kokoszka & Xiangdong Meng, 2024. "Test of change point versus long‐range dependence in functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 497-512, July.
    16. 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.
    17. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
    18. 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.
    19. Horváth, Lajos & Hušková, Marie & Rice, Gregory, 2013. "Test of independence for functional data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 100-119.
    20. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    21. Holger Dette & Pascal Quanz, 2023. "Detecting relevant changes in the spatiotemporal mean function," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 505-532, September.
    22. Tadas Danielius & Alfredas Račkauskas, 2022. "Multiple Change-Point Detection in a Functional Sample via the 𝒢-Sum Process," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    23. Rice, Gregory & Zhang, Chi, 2022. "Consistency of binary segmentation for multiple change-point estimation with functional data," Statistics & Probability Letters, Elsevier, vol. 180(C).
    24. Piotr Kokoszka, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 276-278, June.
    25. J. Derek Tucker & Drew Yarger, 2024. "Elastic functional changepoint detection of climate impacts from localized sources," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    26. Algimantas Birbilas & Alfredas Račkauskas, 2024. "Change-Point Detection in Functional First-Order Auto-Regressive Models," Mathematics, MDPI, vol. 12(12), pages 1-25, June.
    27. 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.
    28. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    29. John Aston, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 256-257, June.
    30. Dennis Schroers, 2024. "Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions," Papers 2401.16286, arXiv.org, revised Jun 2024.
    31. Jialiang Li & Yaguang Li & Tailen Hsing, 2022. "On functional processes with multiple discontinuities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 933-972, July.
    32. Aston, John A.D. & Kirch, Claudia, 2012. "Detecting and estimating changes in dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 204-220.
    33. Fremdt, Stefan & Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef G., 2014. "Functional data analysis with increasing number of projections," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 313-332.
    34. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    35. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    36. 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.
    37. van Delft, Anne & Eichler, Michael, 2017. "Locally Stationary Functional Time Series," LIDAM Discussion Papers ISBA 2017023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    38. Zhou, Jie, 2011. "Maximum likelihood ratio test for the stability of sequence of Gaussian random processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2114-2127, June.

  50. Aue, Alexander & Horváth, Lajos & Reimherr, Matthew L., 2009. "Delay times of sequential procedures for multiple time series regression models," Journal of Econometrics, Elsevier, vol. 149(2), pages 174-190, April.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Knorre, Fabian & Wagner, Martin & Grupe, Maximilian, 2020. "Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions," IHS Working Paper Series 27, Institute for Advanced Studies.
    3. Martin Wagner & Dominik Wied, 2017. "Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 960-980, November.
    4. 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.
    5. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    6. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.
    7. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    8. Eiji Kurozumi, 2021. "Asymptotic Behavior of Delay Times of Bubble Monitoring Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 314-337, May.
    9. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    10. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    11. Christopher Dienes & Alexander Aue, 2014. "On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 239-261, May.

  51. István Berkes & Lajos Horváth & Shiqing Ling, 2009. "Estimation in nonstationary random coefficient autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 395-416, July.

    Cited by:

    1. Aknouche, Abdelhakim & Al-Eid, Eid M. & Hmeid, Aboubakry M., 2011. "Offline and online weighted least squares estimation of nonstationary power ARCH processes," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1535-1540, October.
    2. Abdelhakim Aknouche, 2015. "Quadratic random coefficient autoregression with linear-in-parameters volatility," Statistical Inference for Stochastic Processes, Springer, vol. 18(2), pages 99-125, July.
    3. Min Chen & Dong Li & Shiqing Ling, 2014. "Non-Stationarity And Quasi-Maximum Likelihood Estimation On A Double Autoregressive Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 189-202, May.
    4. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2019. "Random coefficient continuous systems: Testing for extreme sample path behavior," Journal of Econometrics, Elsevier, vol. 209(2), pages 208-237.
    5. Trapani, Lorenzo, 2021. "A test for strict stationarity in a random coefficient autoregressive model of order 1," Statistics & Probability Letters, Elsevier, vol. 177(C).
    6. Nielsen, Heino Bohn & Rahbek, Anders, 2014. "Unit root vector autoregression with volatility induced stationarity," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 144-167.
    7. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    8. Mohammed Benmoumen & Imane Salhi, 2023. "The Strong Consistency of Quasi-Maximum Likelihood Estimators for p-order Random Coefficient Autoregressive (RCA) Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 617-632, February.
    9. Daisuke Nagakura, 2009. "Inconsistency of a Unit Root Test against Stochastic Unit Root Processes," IMES Discussion Paper Series 09-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
    10. Abdelhakim Aknouche, 2012. "Multistage weighted least squares estimation of ARCH processes in the stable and unstable cases," Statistical Inference for Stochastic Processes, Springer, vol. 15(3), pages 241-256, October.
    11. Lajos Horvath & Lorenzo Trapani, 2018. "Testing for randomness in a random coefficient autoregression model," Discussion Papers 18/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    12. Aknouche, Abdelhakim, 2015. "Unified quasi-maximum likelihood estimation theory for stable and unstable Markov bilinear processes," MPRA Paper 69572, University Library of Munich, Germany.
    13. Proïa, Frédéric & Soltane, Marius, 2021. "Comments on the presence of serial correlation in the random coefficients of an autoregressive process," Statistics & Probability Letters, Elsevier, vol. 170(C).
    14. Nagakura, Daisuke, 2009. "Asymptotic theory for explosive random coefficient autoregressive models and inconsistency of a unit root test against a stochastic unit root process," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2476-2483, December.
    15. Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    16. Bercu, Bernard & Blandin, Vassili, 2015. "A Rademacher–Menchov approach for random coefficient bifurcating autoregressive processes," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1218-1243.
    17. Horváth, Lajos & Trapani, Lorenzo, 2016. "Statistical inference in a random coefficient panel model," Journal of Econometrics, Elsevier, vol. 193(1), pages 54-75.
    18. Abdelhakim Aknouche & Eid Al-Eid, 2012. "Asymptotic inference of unstable periodic ARCH processes," Statistical Inference for Stochastic Processes, Springer, vol. 15(1), pages 61-79, April.
    19. Jonathan Hill & Liang Peng, 2014. "Unified Interval Estimation For Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 282-297, May.
    20. Yuxin Pang & Dehui Wang, 2024. "A New Random Coefficient Autoregressive Model Driven by an Unobservable State Variable," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
    21. Zheqi Wang & Dehui Wang & Jianhua Cheng, 2023. "A new autoregressive process driven by explanatory variables and past observations: an application to PM 2.5," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 619-658, June.
    22. Thorsten Fink & Jens-Peter Kreiss, 2013. "Bootstrap For Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 646-667, November.

  52. Lajos Horváth & Remigijus Leipus, 2009. "Effect of aggregation on estimators in AR(1) sequence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 546-567, November.

    Cited by:

    1. Leipus, Remigijus & Philippe, Anne & Pilipauskaitė, Vytautė & Surgailis, Donatas, 2017. "Nonparametric estimation of the distribution of the autoregressive coefficient from panel random-coefficient AR(1) data," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 121-135.

  53. Aue, Alexander & Gabrys, Robertas & Horváth, Lajos & Kokoszka, Piotr, 2009. "Estimation of a change-point in the mean function of functional data," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2254-2269, November.

    Cited by:

    1. Axel Bücher & Holger Dette & Florian Heinrichs, 2020. "Detecting deviations from second-order stationarity in locally stationary functional time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 1055-1094, August.
    2. 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).
    3. 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.
    4. 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.
    5. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    6. 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.
    7. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    8. Woody, Jonathan, 2015. "Time series regression with persistent level shifts," Statistics & Probability Letters, Elsevier, vol. 102(C), pages 22-29.
    9. Stoehr, Christina & Aston, John A D & Kirch, Claudia, 2021. "Detecting changes in the covariance structure of functional time series with application to fMRI data," Econometrics and Statistics, Elsevier, vol. 18(C), pages 44-62.
    10. 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).
    11. Veronika Římalová & Alessandra Menafoglio & Alessia Pini & Vilém Pechanec & Eva Fišerová, 2020. "A permutation approach to the analysis of spatiotemporal geochemical data in the presence of heteroscedasticity," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    12. Changryong Baek & Piotr Kokoszka & Xiangdong Meng, 2024. "Test of change point versus long‐range dependence in functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 497-512, July.
    13. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    14. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    15. Tadas Danielius & Alfredas Račkauskas, 2022. "Multiple Change-Point Detection in a Functional Sample via the 𝒢-Sum Process," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    16. Rice, Gregory & Zhang, Chi, 2022. "Consistency of binary segmentation for multiple change-point estimation with functional data," Statistics & Probability Letters, Elsevier, vol. 180(C).
    17. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    18. J. Derek Tucker & Drew Yarger, 2024. "Elastic functional changepoint detection of climate impacts from localized sources," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    19. Algimantas Birbilas & Alfredas Račkauskas, 2024. "Change-Point Detection in Functional First-Order Auto-Regressive Models," Mathematics, MDPI, vol. 12(12), pages 1-25, June.
    20. 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.
    21. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    22. Dennis Schroers, 2024. "Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions," Papers 2401.16286, arXiv.org, revised Jun 2024.
    23. Jialiang Li & Yaguang Li & Tailen Hsing, 2022. "On functional processes with multiple discontinuities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 933-972, July.
    24. Aston, John A.D. & Kirch, Claudia, 2012. "Detecting and estimating changes in dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 204-220.
    25. van Delft, Anne & Eichler, Michael, 2017. "Locally Stationary Functional Time Series," LIDAM Discussion Papers ISBA 2017023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    26. Zhou, Jie, 2011. "Maximum likelihood ratio test for the stability of sequence of Gaussian random processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2114-2127, June.

  54. Berkes, István & Hörmann, Siegfried & Horváth, Lajos, 2008. "The functional central limit theorem for a family of GARCH observations with applications," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2725-2730, November.

    Cited by:

    1. Marcel Bräutigam & Marie Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," Working Papers hal-02176276, HAL.
    2. Jean-Yves Pitarakis, 2017. "A Simple Approach for Diagnosing Instabilities in Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 851-874, October.
    3. Eunju Hwang, 2021. "Limit Theory for Stationary Autoregression with Heavy-Tailed Augmented GARCH Innovations," Mathematics, MDPI, vol. 9(8), pages 1-10, April.
    4. Jean-Yves Pitarakis, 2020. "A Novel Approach to Predictive Accuracy Testing in Nested Environments," Papers 2008.08387, arXiv.org, revised Oct 2023.
    5. Lee, O., 2013. "The functional central limit theorem for ARMA–GARCH processes," Economics Letters, Elsevier, vol. 121(3), pages 432-435.
    6. Moritz Jirak, 2017. "On Weak Invariance Principles for Partial Sums," Journal of Theoretical Probability, Springer, vol. 30(3), pages 703-728, September.
    7. Lee, Oesook & Lee, Jungwha, 2014. "The functional central limit theorem for the multivariate MS–ARMA–GARCH model," Economics Letters, Elsevier, vol. 125(3), pages 331-335.
    8. Lee, Oesook, 2018. "Stationarity and functional central limit theorem for ARCH(∞) models," Economics Letters, Elsevier, vol. 162(C), pages 107-111.
    9. Moritz Jirak, 2021. "Edgeworth expansions for volatility models," Papers 2111.00529, arXiv.org, revised Sep 2022.
    10. Moritz Jirak, 2016. "Optimal Rate of Convergence for Empirical Quantiles and Distribution Functions for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 825-836, November.
    11. Marcel, Bräutigam & Marie, Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," ESSEC Working Papers WP1909, ESSEC Research Center, ESSEC Business School.

  55. Alexander Aue & Lajos Horváth & Piotr Kokoszka & Josef Steinebach, 2008. "Monitoring shifts in mean: Asymptotic normality of stopping times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 515-530, November.

    Cited by:

    1. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    2. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    3. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.
    4. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    5. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.

  56. Horváth, Lajos & Horváth, Zsuzsanna & Zhou, Wang, 2008. "Asymptotic Properties Of Nonparametric Frontier Estimators," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1607-1627, December.

    Cited by:

    1. Daouia, Abdelaati & Park, Byeong, 2013. "On Projection-type Estimators of Multivariate Isotonic Functions," LIDAM Reprints ISBA 2013020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  57. Aue, Alexander & Horváth, Lajos, 2007. "A Limit Theorem For Mildly Explosive Autoregression With Stable Errors," Econometric Theory, Cambridge University Press, vol. 23(2), pages 201-220, April.

    Cited by:

    1. Xinghui Wang & Wenjing Geng & Ruidong Han & Qifa Xu, 2023. "Asymptotic Properties of the M-estimation for an AR(1) Process with a General Autoregressive Coefficient," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-23, March.
    2. Wang, Xiaohu & Yu, Jun, 2016. "Double asymptotics for explosive continuous time models," Journal of Econometrics, Elsevier, vol. 193(1), pages 35-53.
    3. Christis Katsouris, 2022. "Asymptotic Theory for Unit Root Moderate Deviations in Quantile Autoregressions and Predictive Regressions," Papers 2204.02073, arXiv.org, revised Aug 2023.
    4. Junichi Hirukawa & Sangyeol Lee, 2021. "Asymptotic properties of mildly explosive processes with locally stationary disturbance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 511-534, May.
    5. Magdalinos, Tassos, 2012. "Mildly explosive autoregression under weak and strong dependence," Journal of Econometrics, Elsevier, vol. 169(2), pages 179-187.
    6. Zhou, Zhiyong & Lin, Zhengyan, 2014. "Asymptotic theory for LAD estimation of moderate deviations from a unit root," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 25-32.
    7. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    8. Nannan Ma & Hailin Sang & Guangyu Yang, 2023. "Least absolute deviation estimation for AR(1) processes with roots close to unity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(5), pages 799-832, October.
    9. Gao, Min & Yang, Wenzhi & Wu, Shipeng & Yu, Wei, 2022. "Asymptotic normality of residual density estimator in stationary and explosive autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    10. Tassos Magdalinos, 2008. "Mildly explosive autoregression under weak and strong dependence," Discussion Papers 08/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    11. Stelios Arvanitis & Tassos Magdalinos, 2018. "Mildly Explosive Autoregression Under Stationary Conditional Heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 892-908, November.
    12. Bingqi Liu & Tianxiao Pang, 2024. "Weighted composite quantile inference for nearly nonstationary autoregressive models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1337-1379, November.

  58. Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef, 2007. "On sequential detection of parameter changes in linear regression," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 885-895, May.

    Cited by:

    1. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    2. Chen, Zhanshou & Tian, Zheng, 2010. "Modified procedures for change point monitoring in linear models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 62-75.
    3. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    4. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    5. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    6. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    7. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    8. T. Górecki & Ł. Smaga, 2017. "Multivariate analysis of variance for functional data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2172-2189, September.

  59. Horváth, Lajos & Shao, Qi-Man, 2007. "Limit theorems for permutations of empirical processes with applications to change point analysis," Stochastic Processes and their Applications, Elsevier, vol. 117(12), pages 1870-1888, December.

    Cited by:

    1. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    2. Holmes, Mark & Kojadinovic, Ivan & Quessy, Jean-François, 2013. "Nonparametric tests for change-point detection à la Gombay and Horváth," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 16-32.

  60. Alexander Aue & Lajos Horváth & Josef Steinebach, 2006. "Estimation in Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 61-76, January.

    Cited by:

    1. Abdelhakim Aknouche, 2015. "Quadratic random coefficient autoregression with linear-in-parameters volatility," Statistical Inference for Stochastic Processes, Springer, vol. 18(2), pages 99-125, July.
    2. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2019. "Random coefficient continuous systems: Testing for extreme sample path behavior," Journal of Econometrics, Elsevier, vol. 209(2), pages 208-237.
    3. Trapani, Lorenzo, 2021. "A test for strict stationarity in a random coefficient autoregressive model of order 1," Statistics & Probability Letters, Elsevier, vol. 177(C).
    4. Nielsen, Heino Bohn & Rahbek, Anders, 2014. "Unit root vector autoregression with volatility induced stationarity," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 144-167.
    5. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    6. Mohammed Benmoumen & Imane Salhi, 2023. "The Strong Consistency of Quasi-Maximum Likelihood Estimators for p-order Random Coefficient Autoregressive (RCA) Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 617-632, February.
    7. Johansen, Søren & Lange, Theis, 2013. "Least squares estimation in a simple random coefficient autoregressive model," Journal of Econometrics, Elsevier, vol. 177(2), pages 285-288.
    8. Daisuke Nagakura, 2009. "Inconsistency of a Unit Root Test against Stochastic Unit Root Processes," IMES Discussion Paper Series 09-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
    9. Lajos Horvath & Lorenzo Trapani, 2018. "Testing for randomness in a random coefficient autoregression model," Discussion Papers 18/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    10. Aknouche, Abdelhakim, 2015. "Unified quasi-maximum likelihood estimation theory for stable and unstable Markov bilinear processes," MPRA Paper 69572, University Library of Munich, Germany.
    11. Boukhiar, Souad & Mourid, Tahar, 2022. "Resolvent estimators for functional autoregressive processes with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    12. Proïa, Frédéric & Soltane, Marius, 2021. "Comments on the presence of serial correlation in the random coefficients of an autoregressive process," Statistics & Probability Letters, Elsevier, vol. 170(C).
    13. Nagakura, Daisuke, 2009. "Asymptotic theory for explosive random coefficient autoregressive models and inconsistency of a unit root test against a stochastic unit root process," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2476-2483, December.
    14. Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    15. Bercu, Bernard & Blandin, Vassili, 2015. "A Rademacher–Menchov approach for random coefficient bifurcating autoregressive processes," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1218-1243.
    16. Horváth, Lajos & Trapani, Lorenzo, 2016. "Statistical inference in a random coefficient panel model," Journal of Econometrics, Elsevier, vol. 193(1), pages 54-75.
    17. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    18. Daisuke Nagakura, 2007. "Testing for Coefficient Stability of AR(1) Model When the Null is an Integrated or a Stationary Process," IMES Discussion Paper Series 07-E-20, Institute for Monetary and Economic Studies, Bank of Japan.
    19. Jonathan Hill & Liang Peng, 2014. "Unified Interval Estimation For Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 282-297, May.
    20. István Berkes & Lajos Horváth & Shiqing Ling, 2009. "Estimation in nonstationary random coefficient autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 395-416, July.
    21. Yuxin Pang & Dehui Wang, 2024. "A New Random Coefficient Autoregressive Model Driven by an Unobservable State Variable," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
    22. Aknouche, Abdelhakim & Gouveia, Sonia & Scotto, Manuel, 2023. "Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs," MPRA Paper 119518, University Library of Munich, Germany, revised 18 Dec 2023.
    23. Autcha Araveeporn, 2020. "Comparing Parameter Estimation of Random Coefficient Autoregressive Model by Frequentist Method," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
    24. Zheqi Wang & Dehui Wang & Jianhua Cheng, 2023. "A new autoregressive process driven by explanatory variables and past observations: an application to PM 2.5," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 619-658, June.
    25. Thorsten Fink & Jens-Peter Kreiss, 2013. "Bootstrap For Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 646-667, November.
    26. Yoon, Gawon, 2016. "Stochastic unit root processes: Maximum likelihood estimation, and new Lagrange multiplier and likelihood ratio tests," Economic Modelling, Elsevier, vol. 52(PB), pages 725-732.

  61. Alexander Aue & Lajos Horváth & Marie Hušková & Piotr Kokoszka, 2006. "Change-point monitoring in linear models," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 373-403, November.

    Cited by:

    1. Axel Gandy & Jan Terje Kvaløy, 2013. "Guaranteed Conditional Performance of Control Charts via Bootstrap Methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 647-668, December.
    2. Bardet, Jean-Marc & Kengne, William, 2014. "Monitoring procedure for parameter change in causal time series," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 204-221.
    3. Alexander Aue & Lajos Horváth & Piotr Kokoszka & Josef Steinebach, 2008. "Monitoring shifts in mean: Asymptotic normality of stopping times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 515-530, November.
    4. Aue, Alexander & Horváth, Lajos & Reimherr, Matthew L., 2009. "Delay times of sequential procedures for multiple time series regression models," Journal of Econometrics, Elsevier, vol. 149(2), pages 174-190, April.
    5. Claudia Kirch & Christina Stoehr, 2022. "Sequential change point tests based on U‐statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1184-1214, September.
    6. Marie Hušková & Claudia Kirch, 2012. "Bootstrapping sequential change-point tests for linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 673-708, July.
    7. Chen, Zhanshou & Tian, Zheng, 2010. "Modified procedures for change point monitoring in linear models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 62-75.
    8. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    9. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    10. 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.
    11. Aue, Alexander & Horváth, Lajos & Hušková, Marie, 2012. "Segmenting mean-nonstationary time series via trending regressions," Journal of Econometrics, Elsevier, vol. 168(2), pages 367-381.
    12. Chochola, Ondřej & Hušková, Marie & Prášková, Zuzana & Steinebach, Josef G., 2013. "Robust monitoring of CAPM portfolio betas," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 374-395.
    13. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    14. Amitava Mukherjee, 2013. "Nonparametric Phase-II monitoring for detecting monotone trend based on inverse sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 131-153, June.
    15. Eiji Kurozumi, 2021. "Asymptotic Behavior of Delay Times of Bubble Monitoring Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 314-337, May.
    16. Otto, Sven & Breitung, Jörg, 2020. "Backward CUSUM for Testing and Monitoring Structural Change," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224533, Verein für Socialpolitik / German Economic Association.
    17. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    18. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    19. Sven Otto & Jorg Breitung, 2020. "Backward CUSUM for Testing and Monitoring Structural Change with an Application to COVID-19 Pandemic Data," Papers 2003.02682, arXiv.org, revised Mar 2022.
    20. Christopher Dienes & Alexander Aue, 2014. "On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 239-261, May.

  62. Horvath, Lajos & Kokoszka, Piotr & Zitikis, Ricardas, 2006. "Testing for stochastic dominance using the weighted McFadden-type statistic," Journal of Econometrics, Elsevier, vol. 133(1), pages 191-205, July.

    Cited by:

    1. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    2. Stelios Arvanitis & O. Scaillet & Nikolas Topaloglou, 2020. "Spanning analysis of stock market anomalies under Prospect Stochastic Dominance," Swiss Finance Institute Research Paper Series 20-18, Swiss Finance Institute.
    3. Topaloglou, Nikolas & Tsionas, Mike G., 2020. "Stochastic dominance tests," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    4. Isabel Abinzano & Luis Muga & Rafael Santamaria, 2010. "Do Managerial Skills Vary Across Fund Managers? Results Using European Mutual Funds," Journal of Financial Services Research, Springer;Western Finance Association, vol. 38(1), pages 41-67, August.
    5. Lok, Thomas M. & Tabri, Rami V., 2021. "An improved bootstrap test for restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 224(2), pages 371-393.
    6. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian Assessment of Lorenz and Stochastic Dominance," Department of Economics - Working Papers Series 2029, The University of Melbourne.
    7. Stelios Arvanitis & O. Scaillet & Nikolas Topaloglou, 2018. "Spanning Tests for Markowitz Stochastic Dominance," Swiss Finance Institute Research Paper Series 18-08, Swiss Finance Institute.
    8. Oliver Linton & Kyungchul Song & Yoon-Jae Whang, 2009. "An Improved Bootstrap Test of Stochastic Dominance," Cowles Foundation Discussion Papers 1713, Cowles Foundation for Research in Economics, Yale University.
    9. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers 53/13, Institute for Fiscal Studies.
    10. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.
    11. Stengos, Thanasis & Thompson, Brennan S., 2012. "Testing for bivariate stochastic dominance using inequality restrictions," Economics Letters, Elsevier, vol. 115(1), pages 60-62.
    12. Stelios Arvanitis & Nikolas Topaloglou, 2015. "Consistent tests for risk seeking behavior: A stochastic dominance approach," Working Papers 201511, Athens University Of Economics and Business, Department of Economics.
    13. Lok, Thomas M. & Tabri, Rami V., 2015. "An Improved Bootstrap Test For Restricted Stochastic Dominance," Working Papers 2015-15, University of Sydney, School of Economics, revised Aug 2019.
    14. Bruce L. Jones & Ricardas Zitikis, 2005. "Testing for the order of risk measures: an application of L-statistics in actuarial science," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 193-211.
    15. Sokbae (Simon) Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Mehmet Pinar, 2015. "Measuring world governance: revisiting the institutions hypothesis," Empirical Economics, Springer, vol. 48(2), pages 747-778, March.
    17. Dentcheva Darinka & Stock Gregory J. & Rekeda Ludmyla, 2011. "Mean-risk tests of stochastic dominance," Statistics & Risk Modeling, De Gruyter, vol. 28(2), pages 97-118, May.
    18. Christopher J. Bennett, 2013. "Inference For Dominance Relations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(4), pages 1309-1328, November.
    19. Stelios Arvanitis, 2015. "Saddle-Type Functionals for Continuous Processes with Applications to Tests for Stochastic Spanning," Working Papers 201509, Athens University Of Economics and Business, Department of Economics.
    20. Vivek Dehejia & Marcel Voia, 2008. "International Income Comparisons and Location Choice: Methodology, Analysis, and Implications," Carleton Economic Papers 08-02, Carleton University, Department of Economics.
    21. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    22. Bram Thuysbaert & Ricardas Zitikis, 2005. "Consistent Testing for Poverty Dominance," WIDER Working Paper Series RP2005-64, World Institute for Development Economic Research (UNU-WIDER).
    23. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    24. Stelios Arvanitis, 2021. "Stochastic dominance efficient sets and stochastic spanning," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 401-409, June.
    25. Oliver Linton & Kyungchul Song & Yoon-Jae Whang, 2008. "Bootstrap Tests of Stochastic Dominance with AsymptoticSimilarity on the Boundary," STICERD - Econometrics Paper Series 527, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    26. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    27. Ng, Pin & Wong, Wing-Keung & Xiao, Zhijie, 2017. "Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency," European Journal of Operational Research, Elsevier, vol. 261(2), pages 666-678.
    28. Chuang, O-Chia & Kuan, Chung-Ming & Tzeng, Larry Y., 2017. "Testing for central dominance: Method and application," Journal of Econometrics, Elsevier, vol. 196(2), pages 368-378.
    29. Tabri, Rami V., 2015. "Empirical Likelihood for Robust Poverty Comparisons," Working Papers 2015-02, University of Sydney, School of Economics, revised May 2015.

  63. Berkes, István & Horváth, Lajos, 2006. "Convergence Of Integral Functionals Of Stochastic Processes," Econometric Theory, Cambridge University Press, vol. 22(2), pages 304-322, April.

    Cited by:

    1. Pötscher, Benedikt M., 2011. "On the Order of Magnitude of Sums of Negative Powers of Integrated Processes," MPRA Paper 28287, University Library of Munich, Germany.
    2. Hu, Zhishui & Phillips, Peter C.B. & Wang, Qiying, 2021. "Nonlinear Cointegrating Power Function Regression With Endogeneity," Econometric Theory, Cambridge University Press, vol. 37(6), pages 1173-1213, December.
    3. Peter C.B. Phillips, 2008. "Local Limit Theory and Spurious Nonparametric Regression," Cowles Foundation Discussion Papers 1654, Cowles Foundation for Research in Economics, Yale University.
    4. Qiying Wang & Peter C.B. Phillips, 2006. "Asymptotic Theory for Local Time Density Estimation and Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1594, Cowles Foundation for Research in Economics, Yale University.
    5. Jiti Gao & Peter C.B. Phillips, 2011. "Semiparametric Estimation in Multivariate Nonstationary Time Series Models," Monash Econometrics and Business Statistics Working Papers 17/11, Monash University, Department of Econometrics and Business Statistics.
    6. Ioannis Kasparis & Elena Andreou & Peter C.B. Phillips, 2012. "Nonparametric Predictive Regression," Cowles Foundation Discussion Papers 1878, Cowles Foundation for Research in Economics, Yale University.
    7. Cai, Zongwu & Li, Qi & Park, Joon Y., 2009. "Functional-coefficient models for nonstationary time series data," Journal of Econometrics, Elsevier, vol. 148(2), pages 101-113, February.
    8. Berenguer Rico, Vanessa, 2013. "Co-summability from linear to non-linear cointegration," UC3M Working papers. Economics we1312, Universidad Carlos III de Madrid. Departamento de Economía.
    9. Chan, Nigel & Wang, Qiying, 2015. "Nonlinear regressions with nonstationary time series," Journal of Econometrics, Elsevier, vol. 185(1), pages 182-195.
    10. Ioannis Kasparis & Peter C.B. Phillips, 2009. "Dynamic Misspecification in Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1700, Cowles Foundation for Research in Economics, Yale University.
    11. Gao, Jiti & Phillips, Peter C.B., 2013. "Semiparametric estimation in triangular system equations with nonstationarity," Journal of Econometrics, Elsevier, vol. 176(1), pages 59-79.
    12. Berenguer-Rico, Vanessa & Gonzalo, Jesús, 2014. "Summability of stochastic processes—A generalization of integration for non-linear processes," Journal of Econometrics, Elsevier, vol. 178(P2), pages 331-341.

  64. Horváth, Lajos & Kokoszka, Piotr & Zhang, Aonan, 2006. "Monitoring Constancy Of Variance In Conditionally Heteroskedastic Time Series," Econometric Theory, Cambridge University Press, vol. 22(3), pages 373-402, June.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Cavaliere, Giuseppe & Taylor, A.M. Robert, 2008. "Testing for a change in persistence in the presence of non-stationary volatility," Journal of Econometrics, Elsevier, vol. 147(1), pages 84-98, November.
    3. Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
    4. Wang, Xinyu & Luo, Yi & Wang, Zhuqing & Xu, Yan & Wu, Congxin, 2021. "The impact of economic policy uncertainty on volatility of China’s financial stocks: An empirical analysis," Finance Research Letters, Elsevier, vol. 39(C).
    5. Luis Alberiko Gil-Alaña & Goodness C. Aye & Rangan Gupta, 2013. "Testing for persistence with breaks and outliers in South African house prices," NCID Working Papers 01/2013, Navarra Center for International Development, University of Navarra.
    6. Chen, Zhanshou & Tian, Zheng, 2010. "Modified procedures for change point monitoring in linear models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 62-75.
    7. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2011. "Analyzing the structural behavior of volatility in the Major European Markets during the Greek crisis," Economics Bulletin, AccessEcon, vol. 31(4), pages 3016-3029.
    8. Hsu, Chih-Chiang, 2007. "The MOSUM of squares test for monitoring variance changes," Finance Research Letters, Elsevier, vol. 4(4), pages 254-260, December.
    9. Elena Andreou & Eric Ghysels, 2007. "Quality Control for Structural Credit Risk Models," University of Cyprus Working Papers in Economics 3-2007, University of Cyprus Department of Economics.
    10. Chen, Zhanshou & Xing, Yuhong & Li, Fuxiao, 2016. "Sieve bootstrap monitoring for change from short to long memory," Economics Letters, Elsevier, vol. 140(C), pages 53-56.
    11. Wang, Xinyu & Qi, Zikang & Huang, Jianglu, 2023. "How do monetary shock, financial crisis, and quotation reform affect the long memory of exchange rate volatility? Evidence from major currencies," Economic Modelling, Elsevier, vol. 120(C).
    12. Robert Akunga & Ahmad Hassan Ahmad & Simeon Coleman, 2023. "Financial market integration in sub‐Saharan Africa: How important is contagion?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3637-3653, October.

  65. Horváth, Lajos & Zitikis, Ričardas, 2006. "Testing Goodness Of Fit Based On Densities Of Garch Innovations," Econometric Theory, Cambridge University Press, vol. 22(3), pages 457-482, June.

    Cited by:

    1. Mimoto, Nao, 2008. "Convergence in distribution for the sup-norm of a kernel density estimator for GARCH innovations," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 915-923, May.
    2. Klar, B. & Lindner, F. & Meintanis, S.G., 2012. "Specification tests for the error distribution in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3587-3598.
    3. Sangyeol Lee & Hiroki Masuda, 2010. "Jarque–Bera normality test for the driving Lévy process of a discretely observed univariate SDE," Statistical Inference for Stochastic Processes, Springer, vol. 13(2), pages 147-161, June.
    4. Zhu, Ke, 2015. "Hausman tests for the error distribution in conditionally heteroskedastic models," MPRA Paper 66991, University Library of Munich, Germany.
    5. Donghang Luo & Ke Zhu & Huan Gong & Dong Li, 2020. "Testing error distribution by kernelized Stein discrepancy in multivariate time series models," Papers 2008.00747, arXiv.org.
    6. Hira Koul & Nao Mimoto, 2012. "A goodness-of-fit test for GARCH innovation density," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 127-149, January.

  66. Lajos Horváth & Piotr Kokoszka & Ricardas Zitikis, 2006. "Sample and Implied Volatility in GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 617-635.

    Cited by:

    1. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2009. "Merits and drawbacks of variance targeting in GARCH models," MPRA Paper 15143, University Library of Munich, Germany.
    2. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    3. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    4. Antonis Demos, 2023. "Statistical Properties of Two Asymmetric Stochastic Volatility in Mean Models," DEOS Working Papers 2303, Athens University of Economics and Business.
    5. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Stanislav Khrapov, 2011. "Pricing Central Tendency in Volatility," Working Papers w0168, Center for Economic and Financial Research (CEFIR).
    7. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    8. 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.

  67. Berkes, Istvan & Horváth, Lajos & Kokoszka, Piotr, 2004. "Testing for parameter constancy in GARCH(p,q) models," Statistics & Probability Letters, Elsevier, vol. 70(4), pages 263-273, December.

    Cited by:

    1. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    2. Cavaliere, Giuseppe & Taylor, A.M. Robert, 2008. "Testing for a change in persistence in the presence of non-stationary volatility," Journal of Econometrics, Elsevier, vol. 147(1), pages 84-98, November.
    3. Haejune Oh & Sangyeol Lee, 2018. "On score vector- and residual-based CUSUM tests in ARMA–GARCH models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 385-406, August.
    4. Song, Junmo & Kang, Jiwon, 2018. "Parameter change tests for ARMA–GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 41-56.
    5. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.
    6. Youngmi Lee & Sangyeol Lee, 2019. "CUSUM test for general nonlinear integer-valued GARCH models: comparison study," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1033-1057, October.
    7. Kang, Jiwon & Song, Junmo, 2017. "Score test for parameter change in Poisson autoregressive models," Economics Letters, Elsevier, vol. 160(C), pages 33-37.
    8. Haejune Oh & Sangyeol Lee, 2019. "Modified residual CUSUM test for location-scale time series models with heteroscedasticity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1059-1091, October.
    9. Lee, Sangyeol & Song, Junmo, 2008. "Test for parameter change in ARMA models with GARCH innovations," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1990-1998, September.
    10. Chunliang Deng & Xingfa Zhang & Yuan Li & Qiang Xiong, 2020. "Garch Model Test Using High-Frequency Data," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    11. Haipeng Xing & Hongsong Yuan & Sichen Zhou, 2017. "A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points," Review of Economics & Finance, Better Advances Press, Canada, vol. 8, pages 44-60, May.

  68. Aue, Alexander & Horváth, Lajos, 2004. "Delay time in sequential detection of change," Statistics & Probability Letters, Elsevier, vol. 67(3), pages 221-231, April.

    Cited by:

    1. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).
    2. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    3. Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef, 2007. "On sequential detection of parameter changes in linear regression," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 885-895, May.
    4. Timmermann, Hella, 2015. "Sequential detection of gradual changes in the location of a general stochastic process," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 85-93.
    5. Alexander Aue & Lajos Horváth & Piotr Kokoszka & Josef Steinebach, 2008. "Monitoring shifts in mean: Asymptotic normality of stopping times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 515-530, November.
    6. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    7. Aue, Alexander & Horváth, Lajos & Reimherr, Matthew L., 2009. "Delay times of sequential procedures for multiple time series regression models," Journal of Econometrics, Elsevier, vol. 149(2), pages 174-190, April.
    8. Chen, Zhanshou & Tian, Zheng, 2010. "Modified procedures for change point monitoring in linear models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 62-75.
    9. Josef Steinebach, 2009. "Monitoring risk in a ruin model perturbed by diffusion," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(2), pages 205-224, September.
    10. 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.
    11. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    12. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    13. Aue, Alexander, 2004. "Strong approximation for RCA(1) time series with applications," Statistics & Probability Letters, Elsevier, vol. 68(4), pages 369-382, July.
    14. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    15. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.
    16. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
    17. Šárka Hudecová & Marie Hušková & Simos G. Meintanis, 2017. "Tests for Structural Changes in Time Series of Counts," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 843-865, December.
    18. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    19. Eiji Kurozumi, 2021. "Asymptotic Behavior of Delay Times of Bubble Monitoring Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 314-337, May.
    20. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    21. Mihalache, Stefan, 2012. "Strong approximations and sequential change-point analysis for diffusion processes," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 464-472.
    22. Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2009. "Sufficient reduction in multivariate surveillance," Research Reports 2009:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

  69. Berkes, István & Gombay, Edit & Horváth, Lajos & Kokoszka, Piotr, 2004. "SEQUENTIAL CHANGE-POINT DETECTION IN GARCH(p,q) MODELS," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1140-1167, December.

    Cited by:

    1. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).
    2. Christina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," CREATES Research Papers 2008-08, Department of Economics and Business Economics, Aarhus University.
    3. Bardet, Jean-Marc & Kengne, William, 2014. "Monitoring procedure for parameter change in causal time series," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 204-221.
    4. Lee, Sangyeol & Park, Siyun, 2009. "The monitoring test for the stability of regression models with nonstationary regressors," Economics Letters, Elsevier, vol. 105(3), pages 250-252, December.
    5. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 77-124.
    6. Haejune Oh & Sangyeol Lee, 2018. "On score vector- and residual-based CUSUM tests in ARMA–GARCH models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 385-406, August.
    7. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    8. Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
    9. Isakov , Alexander, 2013. "Stress indicator construction for internal money market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 77-92.
    10. Song, Junmo & Kang, Jiwon, 2018. "Parameter change tests for ARMA–GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 41-56.
    11. Nasri, Bouchra R. & Rémillard, Bruno N. & Bahraoui, Tarik, 2022. "Change-point problems for multivariate time series using pseudo-observations," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    12. Aue, Alexander & Horváth, Lajos & Reimherr, Matthew L., 2009. "Delay times of sequential procedures for multiple time series regression models," Journal of Econometrics, Elsevier, vol. 149(2), pages 174-190, April.
    13. Cristina Amado & Timo Teräsvirta, 2011. "Modelling Volatility by Variance Decomposition," NIPE Working Papers 01/2011, NIPE - Universidade do Minho.
    14. Stefan Richter & Weining Wang & Wei Biao Wu, 2023. "Testing for parameter change epochs in GARCH time series," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 467-491.
    15. Mihaela Craioveanu & Eric Hillebrand, 2012. "Level changes in volatility models," Annals of Finance, Springer, vol. 8(2), pages 277-308, May.
    16. 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.
    17. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.
    18. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    19. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    20. Richard A. Davis & Thomas C. M. Lee & Gabriel A. Rodriguez‐Yam, 2008. "Break Detection for a Class of Nonlinear Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 834-867, September.
    21. 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.
    22. Lee, Youngmi & Kim, Sungdon & Oh, Haejune, 2024. "Sequential change-point detection in time series models with conditional heteroscedasticity," Economics Letters, Elsevier, vol. 236(C).
    23. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    24. 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.
    25. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    26. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    27. Błażej Mazur & Mateusz Pipień, 2012. "On the Empirical Importance of Periodicity in the Volatility of Financial Returns - Time Varying GARCH as a Second Order APC(2) Process," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(2), pages 95-116, June.
    28. Christopher Dienes & Alexander Aue, 2014. "On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 239-261, May.
    29. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    30. Okyoung Na & Youngmi Lee & Sangyeol Lee, 2011. "Monitoring parameter change in time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 171-199, June.
    31. Haipeng Xing & Hongsong Yuan & Sichen Zhou, 2017. "A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points," Review of Economics & Finance, Better Advances Press, Canada, vol. 8, pages 44-60, May.
    32. Zhou, Yong & Wan, Alan T.K. & Xie, Shangyu & Wang, Xiaojing, 2010. "Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 183-201, November.

  70. Berkes, István & Horváth, Lajos, 2003. "Limit results for the empirical process of squared residuals in GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 105(2), pages 271-298, June.

    Cited by:

    1. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    2. TEYSSIERE, Gilles, 2003. "Interaction models for common long-range dependence in asset price volatilities," LIDAM Discussion Papers CORE 2003026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Chandra, S. Ajay, 2009. "Testing the equality of error distributions from k independent GARCH models," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1245-1260, July.
    4. de Pooter, M.D. & van Dijk, D.J.C., 2004. "Testing for changes in volatility in heteroskedastic time series - a further examination," Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Kirman, Alan & Teyssiere, Gilles, 2005. "Testing for bubbles and change-points," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 765-799, April.
    6. Li, Deyuan & Peng, Liang, 2009. "Goodness-of-fit test for tail copulas modeled by elliptical copulas," Statistics & Probability Letters, Elsevier, vol. 79(8), pages 1097-1104, April.

  71. Berkes, István & Horváth, Lajos & Kokoszka, Piotr, 2003. "Asymptotics For Garch Squared Residual Correlations," Econometric Theory, Cambridge University Press, vol. 19(4), pages 515-540, August.

    Cited by:

    1. Hidalgo, Javier & Zaffaroni, Paolo, 2007. "A goodness-of-fit test for ARCH([infinity]) models," Journal of Econometrics, Elsevier, vol. 141(2), pages 835-875, December.
    2. Yi-Ting Chen, 2008. "A unified approach to standardized-residuals-based correlation tests for GARCH-type models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 111-133.
    3. Francq, Christian & Zakoïan, Jean-Michel, 2010. "Inconsistency of the MLE and inference based on weighted LS for LARCH models," Journal of Econometrics, Elsevier, vol. 159(1), pages 151-165, November.
    4. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    5. Royer, Julien, 2023. "Conditional asymmetry in Power ARCH(∞) models," Journal of Econometrics, Elsevier, vol. 234(1), pages 178-204.
    6. 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.
    7. Royer, Julien, 2021. "Conditional asymmetry in Power ARCH($\infty$) models," MPRA Paper 109118, University Library of Munich, Germany.
    8. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 9fe68e51-a026-4660-b6e7-8, Tilburg University, School of Economics and Management.
    9. Yun Gong & Zhouping Li & Liang Peng, 2010. "Empirical likelihood intervals for conditional Value‐at‐Risk in ARCH/GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 65-75, March.
    10. Zhu, Fukang & Wang, Dehui, 2010. "Diagnostic checking integer-valued ARCH(p) models using conditional residual autocorrelations," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 496-508, February.
    11. Ben, Youhong & Jiang, Feiyu, 2020. "A note on Portmanteau tests for conditional heteroscedastistic models," Economics Letters, Elsevier, vol. 192(C).
    12. Kilani Ghoudi & Bruno Rémillard, 2018. "Serial independence tests for innovations of conditional mean and variance models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 3-26, March.
    13. 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.
    14. Andreou, Elena & Werker, Bas J.M., 2015. "Residual-based rank specification tests for AR–GARCH type models," Journal of Econometrics, Elsevier, vol. 185(2), pages 305-331.
    15. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2004-56, Tilburg University, Center for Economic Research.
    16. Elena Andreou & Bas J.M. Werker, 2014. "Residual-based Rank Specification Tests for AR-GARCH type models," University of Cyprus Working Papers in Economics 02-2014, University of Cyprus Department of Economics.
    17. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 93fe16c1-9f21-4dab-9b73-4, Tilburg University, School of Economics and Management.
    18. Christian FRANCQ & Jean-Michel ZAKOIAN, 2009. "Properties of the QMLE and the Weighted LSE for LARCH(q) Models," Working Papers 2009-19, Center for Research in Economics and Statistics.
    19. Werker, Bas J M & Andreou, Elena, 2013. "Residual-based Rank Specification Tests for AR-GARCH type models," CEPR Discussion Papers 9583, C.E.P.R. Discussion Papers.
    20. Yacouba Boubacar Maïnassara & Othman Kadmiri & Bruno Saussereau, 2022. "Portmanteau test for the asymmetric power GARCH model when the power is unknown," Statistical Papers, Springer, vol. 63(3), pages 755-793, June.
    21. Li, Yuanbo & Ng, Chi Tim & Yau, Chun Yip, 2022. "GARCH-type factor model," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    22. Das, Suman & Roy, Saikat Sinha, 2023. "Following the leaders? A study of co-movement and volatility spillover in BRICS currencies," Economic Systems, Elsevier, vol. 47(2).
    23. Ghoudi, Kilani & Rémillard, Bruno, 2014. "Comparison of specification tests for GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 291-300.
    24. Carbon, Michel & Francq, Christian, 2010. "Portmanteau goodness-of-fit test for asymmetric power GARCH models," MPRA Paper 27686, University Library of Munich, Germany.
    25. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2003-118, Tilburg University, Center for Economic Research.

  72. Berkes, István & Horváth, Lajos & Kokoszka, Piotr, 2003. "Estimation Of The Maximal Moment Exponent Of A Garch(1,1) Sequence," Econometric Theory, Cambridge University Press, vol. 19(4), pages 565-586, August.

    Cited by:

    1. Iglesias, Emma M., 2015. "Value at Risk of the main stock market indexes in the European Union (2000–2012)," Journal of Policy Modeling, Elsevier, vol. 37(1), pages 1-13.
    2. Su, Xiaoshan & Li, Yuhan, 2024. "Robust portfolio selection with subjective risk aversion under dependence uncertainty," Economic Modelling, Elsevier, vol. 132(C).
    3. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Francq, Christian & Zakoian, Jean-Michel, 2024. "Finite moments testing in a general class of nonlinear time series models," MPRA Paper 121193, University Library of Munich, Germany.
    5. Francq, Christian & Zakoian, Jean-Michel, 2021. "Testing the existence of moments and estimating the tail index of augmented garch processes," MPRA Paper 110511, University Library of Munich, Germany.
    6. Beran, Jan & Schell, Dieter, 2012. "On robust tail index estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3430-3443.
    7. Polzehl, Jörg & Spokoiny, Vladimir, 2006. "Varying coefficient GARCH versus local constant volatility modeling: Comparison of the predictive power," SFB 649 Discussion Papers 2006-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Vukovic, Darko B. & Lapshina, Kseniya A. & Maiti, Moinak, 2021. "Wavelet coherence analysis of returns, volatility and interdependence of the US and the EU money markets: Pre & post crisis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    9. Agnieszka Jach & Piotr Kokoszka, 2004. "Subsampling Unit Root Tests for Heavy-Tailed Observations," Methodology and Computing in Applied Probability, Springer, vol. 6(1), pages 73-97, March.
    10. Iglesias, Emma M., 2015. "Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation," Economic Modelling, Elsevier, vol. 50(C), pages 1-8.
    11. 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.
    12. Piotr Kokoszka & Michael Wolf, 2004. "Subsampling the mean of heavy‐tailed dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 217-234, March.
    13. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.

  73. Berkes, István & Horváth, Lajos, 2003. "The rate of consistency of the quasi-maximum likelihood estimator," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 133-143, January.

    Cited by:

    1. Rasmus Søndergaard Pedersen & Anders Rahbek, 2015. "Nonstationary ARCH and GARCH with t-Distributed Innovations," Discussion Papers 15-07, University of Copenhagen. Department of Economics.
    2. PREMINGER Arie & STORTI Giuseppe, 2017. "Least squares estimation for GARCH (1,1) model with heavy tailed errors," LIDAM Discussion Papers CORE 2017015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Toda, Alexis Akira & Walsh, Kieran James, 2016. "Fat Tails and Spurious Estimation of Consumption-Based Asset Pricing Models," MPRA Paper 78980, University Library of Munich, Germany.
    4. Taewook Lee & Sangyeol Lee, 2009. "Normal Mixture Quasi‐maximum Likelihood Estimator for GARCH Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 157-170, March.
    5. Maia, Gisele de Oliveira & Barreto-Souza, Wagner & Bastos, Fernando de Souza & Ombao, Hernando, 2021. "Semiparametric time series models driven by latent factor," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1463-1479.
    6. Lopes, Sílvia R.C. & Prass, Taiane S., 2014. "Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 278-307.
    7. PREMINGER, Arie & STORTI, Giuseppe, 2006. "A GARCH (1,1) estimator with (almost) no moment conditions on the error term," LIDAM Discussion Papers CORE 2006068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Berkes, Istvan & Horváth, Lajos & Kokoszka, Piotr, 2004. "Testing for parameter constancy in GARCH(p,q) models," Statistics & Probability Letters, Elsevier, vol. 70(4), pages 263-273, December.
    9. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
    10. Neifar, Malika, 2020. "Multivariate GARCH Approaches: case of major sectorial Tunisian stock markets," MPRA Paper 99658, University Library of Munich, Germany.
    11. Francq, Christian & Zakoian, Jean-Michel, 2007. "Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero," Stochastic Processes and their Applications, Elsevier, vol. 117(9), pages 1265-1284, September.
    12. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    13. Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2016. "Nonstationary GARCH with t-distributed innovations," Economics Letters, Elsevier, vol. 138(C), pages 19-21.

  74. Horváth, Lajos & Zitikis, Ricardas, 2003. "Asymptotics of the Lp-norms of density estimators in the first-order autoregressive models," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 331-342, December.

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    1. Gao, Min & Yang, Wenzhi & Wu, Shipeng & Yu, Wei, 2022. "Asymptotic normality of residual density estimator in stationary and explosive autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    2. Cheng, Fuxia, 2018. "Glivenko–Cantelli Theorem for the kernel error distribution estimator in the first-order autoregressive model," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 95-102.

  75. Horváth, Lajos & Kokoszka, Piotr, 2003. "A bootstrap approximation to a unit root test statistic for heavy-tailed observations," Statistics & Probability Letters, Elsevier, vol. 62(2), pages 163-173, April.

    Cited by:

    1. Jin, Hao & Tian, Zheng & Qin, Ruibing, 2009. "Bootstrap tests for structural change with infinite variance observations," Statistics & Probability Letters, Elsevier, vol. 79(19), pages 1985-1995, October.
    2. Arvanitis, Stelios, 2017. "A note on the limit theory of a Dickey–Fuller unit root test with heavy tailed innovations," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 198-204.
    3. Hao Jin & Si Zhang & Jinsuo Zhang, 2017. "Spurious regression due to neglected of non-stationary volatility," Computational Statistics, Springer, vol. 32(3), pages 1065-1081, September.
    4. Chen, Zhanshou & Jin, Zi & Tian, Zheng & Qi, Peiyan, 2012. "Bootstrap testing multiple changes in persistence for a heavy-tailed sequence," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2303-2316.
    5. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.
    6. Jin, Hao & Zhang, Jinsuo, 2011. "Modified tests for variance changes in autoregressive regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(6), pages 1099-1109.
    7. Qin, Ruibing & Tian, Zheng & Jin, Hao & Zhang, Xiaowei, 2010. "Strong convergence rate of robust estimator of change point," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2026-2032.
    8. Agnieszka Jach & Piotr Kokoszka, 2004. "Subsampling Unit Root Tests for Heavy-Tailed Observations," Methodology and Computing in Applied Probability, Springer, vol. 6(1), pages 73-97, March.
    9. Kirman, Alan & Teyssiere, Gilles, 2005. "Testing for bubbles and change-points," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 765-799, April.
    10. Serttas, Fatma Ozgu, 2010. "Essays on infinite-variance stable errors and robust estimation procedures," ISU General Staff Papers 201001010800002742, Iowa State University, Department of Economics.

  76. Clark, Jim & Horváth, Lajos & Lewis, Mark, 2001. "On the estimation of spread rate for a biological population," Statistics & Probability Letters, Elsevier, vol. 51(3), pages 225-234, February.

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    1. Michael G. Neubert & Ingrid M. Parker, 2004. "Projecting Rates of Spread for Invasive Species," Risk Analysis, John Wiley & Sons, vol. 24(4), pages 817-831, August.
    2. Iftikhar U. Sikder & Sanchita Mal‐Sarkar & Tarun K. Mal, 2006. "Knowledge‐Based Risk Assessment Under Uncertainty for Species Invasion," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 239-252, February.

  77. Berkes, István & Horváth, Lajos, 2001. "The logarithmic average of sample extremes is asymptotically normal," Stochastic Processes and their Applications, Elsevier, vol. 91(1), pages 77-98, January.

    Cited by:

    1. Berkes, István & Weber, Michel, 2006. "Almost sure versions of the Darling-Erdös theorem," Statistics & Probability Letters, Elsevier, vol. 76(3), pages 280-290, February.
    2. Csáki, Endre & Gonchigdanzan, Khurelbaatar, 2002. "Almost sure limit theorems for the maximum of stationary Gaussian sequences," Statistics & Probability Letters, Elsevier, vol. 58(2), pages 195-203, June.
    3. Fahrner, Ingo, 2001. "A strong invariance principle for the logarithmic average of sample maxima," Stochastic Processes and their Applications, Elsevier, vol. 93(2), pages 317-337, June.

  78. Horváth, Lajos, 2001. "Change-Point Detection in Long-Memory Processes," Journal of Multivariate Analysis, Elsevier, vol. 78(2), pages 218-234, August.

    Cited by:

    1. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
    2. Lavancier, Frédéric & Philippe, Anne & Surgailis, Donatas, 2010. "A two-sample test for comparison of long memory parameters," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2118-2136, October.
    3. Husková, M., 2003. "Serial rank statistics for detection of changes," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 199-213, January.

  79. Horváth, Lajos & Kokoszka, Piotr, 2001. "LARGE SAMPLE DISTRIBUTION OF WEIGHTED SUMS OF ARCH(p) SQUARED RESIDUAL CORRELATIONS," Econometric Theory, Cambridge University Press, vol. 17(2), pages 283-295, April.

    Cited by:

    1. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
    2. Francq, Christian & Zakoïan, Jean-Michel, 2010. "Inconsistency of the MLE and inference based on weighted LS for LARCH models," Journal of Econometrics, Elsevier, vol. 159(1), pages 151-165, November.
    3. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 9fe68e51-a026-4660-b6e7-8, Tilburg University, School of Economics and Management.
    4. Zhu, Fukang & Wang, Dehui, 2010. "Diagnostic checking integer-valued ARCH(p) models using conditional residual autocorrelations," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 496-508, February.
    5. Andreou, Elena & Werker, Bas J.M., 2015. "Residual-based rank specification tests for AR–GARCH type models," Journal of Econometrics, Elsevier, vol. 185(2), pages 305-331.
    6. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2004-56, Tilburg University, Center for Economic Research.
    7. Elena Andreou & Bas J.M. Werker, 2014. "Residual-based Rank Specification Tests for AR-GARCH type models," University of Cyprus Working Papers in Economics 02-2014, University of Cyprus Department of Economics.
    8. Andreou, E. & Werker, B.J.M., 2004. "An Alternative Asymptotic Analysis of Residual-Based Statistics," Other publications TiSEM 93fe16c1-9f21-4dab-9b73-4, Tilburg University, School of Economics and Management.
    9. Christian FRANCQ & Jean-Michel ZAKOIAN, 2009. "Properties of the QMLE and the Weighted LSE for LARCH(q) Models," Working Papers 2009-19, Center for Research in Economics and Statistics.
    10. Werker, Bas J M & Andreou, Elena, 2013. "Residual-based Rank Specification Tests for AR-GARCH type models," CEPR Discussion Papers 9583, C.E.P.R. Discussion Papers.
    11. Berkes, István & Horváth, Lajos, 2003. "Limit results for the empirical process of squared residuals in GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 105(2), pages 271-298, June.
    12. Ghoudi, Kilani & Rémillard, Bruno, 2014. "Comparison of specification tests for GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 291-300.
    13. Andreou, E. & Werker, B.J.M., 2003. "A Simple Asymptotic Analysis of Residual-Based Statistics," Discussion Paper 2003-118, Tilburg University, Center for Economic Research.

  80. Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef, 2000. "Approximations for weighted bootstrap processes with an application," Statistics & Probability Letters, Elsevier, vol. 48(1), pages 59-70, May.

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    1. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    2. Liu, Bo & Mojirsheibani, Majid, 2015. "On a weighted bootstrap approximation of the Lp norms of kernel density estimators," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 65-73.
    3. Mojirsheibani, Majid, 2012. "A weighted bootstrap approximation of the maximal deviation of kernel density estimates over general compact sets," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 230-241.
    4. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    5. Sergio Alvarez-Andrade & Salim Bouzebda, 2014. "Asymptotic results for hybrids of empirical and partial sums processes," Statistical Papers, Springer, vol. 55(4), pages 1121-1143, November.
    6. Ali Al-Sharadqah & Majid Mojirsheibani, 2020. "A simple approach to construct confidence bands for a regression function with incomplete data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 81-99, March.
    7. Majid Mojirsheibani & William Pouliot, 2017. "Weighted bootstrapped kernel density estimators in two-sample problems," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 61-84, January.

  81. Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef, 1999. "Testing for Changes in Multivariate Dependent Observations with an Application to Temperature Changes," Journal of Multivariate Analysis, Elsevier, vol. 68(1), pages 96-119, January.

    Cited by:

    1. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.
    2. Ayyala, Deepak Nag & Park, Junyong & Roy, Anindya, 2017. "Mean vector testing for high-dimensional dependent observations," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 136-155.
    3. 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.
    4. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
    5. Elena Andreou & Eric Ghysels, 2001. "Detecting Mutiple Breaks in Financial Market Volatility Dynamics," CIRANO Working Papers 2001s-65, CIRANO.
    6. 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.
    7. Einmahl, J.H.J. & Gantner, M., 2009. "The Half-Half Plot," Discussion Paper 2009-77, Tilburg University, Center for Economic Research.
    8. Elena Andreou & Eric Ghysels, 2002. "Tests for Breaks in the Conditional Co-movements of Asset Returns," CIRANO Working Papers 2002s-59, CIRANO.
    9. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    10. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.
    11. Leonid Torgovitski, 2015. "A Darling–Erdős-type CUSUM-procedure for functional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(1), pages 1-27, January.
    12. Gantner, M., 2010. "Some nonparametric diagnostic statistical procedures and their asymptotic behavior," Other publications TiSEM eb04bdba-bf8a-4f6c-8dd8-9, Tilburg University, School of Economics and Management.
    13. Horváth, Lajos & Husková, Marie & Kokoszka, Piotr, 2010. "Testing the stability of the functional autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 352-367, February.
    14. 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.
    15. Junwei Hu & Lihong Wang, 2023. "A weighted U-statistic based change point test for multivariate time series," Statistical Papers, Springer, vol. 64(3), pages 753-778, June.
    16. Florian Stark & Sven Otto, 2020. "Testing and Dating Structural Changes in Copula-based Dependence Measures," Papers 2011.05036, arXiv.org.
    17. 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.
    18. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    19. Aston, John A.D. & Kirch, Claudia, 2012. "Detecting and estimating changes in dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 204-220.
    20. Aue, Alexander & Gabrys, Robertas & Horváth, Lajos & Kokoszka, Piotr, 2009. "Estimation of a change-point in the mean function of functional data," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2254-2269, November.
    21. Wang Lihong, 2003. "Limit theorems in change-point problems with multivariate long-range dependent observations," Statistics & Risk Modeling, De Gruyter, vol. 21(3), pages 283-300, March.
    22. Zhou, Jie, 2011. "Maximum likelihood ratio test for the stability of sequence of Gaussian random processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2114-2127, June.

  82. Horváth, Lajos & Steinebach, Josef, 1999. "On the best approximation for bootstrapped empirical processes," Statistics & Probability Letters, Elsevier, vol. 41(2), pages 117-122, January.

    Cited by:

    1. Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef, 2000. "Approximations for weighted bootstrap processes with an application," Statistics & Probability Letters, Elsevier, vol. 48(1), pages 59-70, May.

  83. Berkes, István & Horváth, Lajos & Khoshnevisan, Davar, 1998. "Logarithmic averages of stable random variables are asymptotically normal," Stochastic Processes and their Applications, Elsevier, vol. 77(1), pages 35-51, September.

    Cited by:

    1. Berkes, István & Horváth, Lajos, 2001. "The logarithmic average of sample extremes is asymptotically normal," Stochastic Processes and their Applications, Elsevier, vol. 91(1), pages 77-98, January.

  84. Berkes, István & Csáki, Endre & Horváth, Lajos, 1998. "Almost sure central limit theorems under minimal conditions," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 67-76, January.

    Cited by:

    1. Ibragimov, Ildar & Lifshits, Mikhail, 1998. "On the convergence of generalized moments in almost sure central limit theorem," Statistics & Probability Letters, Elsevier, vol. 40(4), pages 343-351, November.
    2. Berkes, István & Horváth, Lajos, 2001. "The logarithmic average of sample extremes is asymptotically normal," Stochastic Processes and their Applications, Elsevier, vol. 91(1), pages 77-98, January.
    3. Zuoxiang Peng & Zhongquan Tan & Saralees Nadarajah, 2011. "Almost sure central limit theorem for the products of U-statistics," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(1), pages 61-76, January.
    4. Fahrner, Ingo, 2000. "An extension of the almost sure max-limit theorem," Statistics & Probability Letters, Elsevier, vol. 49(1), pages 93-103, August.

  85. Lajos Horváth, 1997. "Detection of Changes in Linear Sequences," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(2), pages 271-283, June.

    Cited by:

    1. Elena Andreou & Eric Ghysels, 2001. "Detecting Mutiple Breaks in Financial Market Volatility Dynamics," CIRANO Working Papers 2001s-65, CIRANO.
    2. Aue, Alexander & Horváth, Lajos, 2004. "Delay time in sequential detection of change," Statistics & Probability Letters, Elsevier, vol. 67(3), pages 221-231, April.
    3. Elena Andreou & Eric Ghysels, 2002. "Tests for Breaks in the Conditional Co-movements of Asset Returns," CIRANO Working Papers 2002s-59, CIRANO.
    4. Kühn, Christoph, 2001. "An estimator of the number of change points based on a weak invariance principle," Statistics & Probability Letters, Elsevier, vol. 51(2), pages 189-196, January.
    5. Kokoszka, Piotr & Leipus, Remigijus, 1998. "Change-point in the mean of dependent observations," Statistics & Probability Letters, Elsevier, vol. 40(4), pages 385-393, November.
    6. Aue, Alexander, 2004. "Strong approximation for RCA(1) time series with applications," Statistics & Probability Letters, Elsevier, vol. 68(4), pages 369-382, July.

  86. Aboabboud, M.M. & Horvath, L. & Szépvölgy, J. & Mink, G. & Radhika, E. & Kudish, A.I., 1997. "The use of a thermal energy recycle unit in conjunction with a basin-type solar still for enhanced productivity," Energy, Elsevier, vol. 22(1), pages 83-91.

    Cited by:

    1. Hongfei, Zheng, 2001. "Experimental study on an enhanced falling film evaporation–air flow absorption and closed circulation solar still," Energy, Elsevier, vol. 26(4), pages 401-412.
    2. Hongfei, Zheng & Xinshi, Ge, 2002. "Steady-state experimental study of a closed recycle solar still with enhanced falling film evaporation and regeneration," Renewable Energy, Elsevier, vol. 26(2), pages 295-308.

  87. Csörgő Miklós & Horváth Lajos & Szyszkowicz Barbara, 1997. "Integral Tests For Suprema Of Kiefer Processes With Application," Statistics & Risk Modeling, De Gruyter, vol. 15(4), pages 365-378, April.

    Cited by:

    1. Bouzebda, Salim & Limnios, Nikolaos, 2013. "On general bootstrap of empirical estimator of a semi-Markov kernel with applications," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 52-62.

  88. Horvàth, Lajos & Shao, Qi-Man, 1996. "Darling-Erdos-type theorems for sums of Gaussian variables with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 63(1), pages 117-137, October.

    Cited by:

    1. Moon, Hee-Jin & Choi, Yong-Kab, 2007. "Asymptotic properties for partial sum processes of a Gaussian random field," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 9-18, January.
    2. Qiying Wang & Yan-Xia Lin & Chandra M. Gulati, 2003. "Strong Approximation for Long Memory Processes with Applications," Journal of Theoretical Probability, Springer, vol. 16(2), pages 377-389, April.

  89. Gombay Edit & Horváth Lajos & Husková Marie, 1996. "Estimators And Tests For Change In Variances," Statistics & Risk Modeling, De Gruyter, vol. 14(2), pages 145-160, February.

    Cited by:

    1. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
    2. Cheng, Tsung-Lin, 2009. "An efficient algorithm for estimating a change-point," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 559-565, March.
    3. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.
    4. Stergios B. Fotopoulos & Alex Paparas & Venkata K. Jandhyala, 2022. "Change point detection and estimation methods under gamma series of observations," Statistical Papers, Springer, vol. 63(3), pages 723-754, June.
    5. Lihong Wang & Jinde Wang, 2006. "Change-of-variance problem for linear processes with long memory," Statistical Papers, Springer, vol. 47(2), pages 279-298, March.
    6. Pouliot, W. & Olmo, J., 2008. "U-statistic Type Tests for Structural Breaks in Linear Regression Models," Working Papers 08/15, Department of Economics, City University London.
    7. Augusto Rupérez-Micola & Albert Banal-Estañol, 2007. "Composition of electricity generation portfolios, pivotal dynamics and market prices," Economics Working Papers 1083, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    9. Wenzhi Zhao & Zheng Tian & Zhiming Xia, 2010. "Ratio test for variance change point in linear process with long memory," Statistical Papers, Springer, vol. 51(2), pages 397-407, June.

  90. Csörgo, Miklós & Horváth, Lajos, 1996. "A note on the change-point problem for angular data," Statistics & Probability Letters, Elsevier, vol. 27(1), pages 61-65, March.

    Cited by:

    1. Irina Grabovsky & Lajos Horváth, 2001. "Change-Point Detection in Angular Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(3), pages 552-566, September.
    2. Kaushik Ghosh & S. Rao Jammalamadaka & Mangalam Vasudaven, 1999. "Change-point problems for the von Mises distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 423-434.

  91. Aboabboud, M.M. & Horvath, L. & Mink, G. & Yasin, M. & Kudish, A.I., 1996. "An energy saving atmospheric evaporator utilizing low grade thermal or waste energy," Energy, Elsevier, vol. 21(12), pages 1107-1117.

    Cited by:

    1. Hongfei, Zheng, 2001. "Experimental study on an enhanced falling film evaporation–air flow absorption and closed circulation solar still," Energy, Elsevier, vol. 26(4), pages 401-412.
    2. Hongfei, Zheng & Xinshi, Ge, 2002. "Steady-state experimental study of a closed recycle solar still with enhanced falling film evaporation and regeneration," Renewable Energy, Elsevier, vol. 26(2), pages 295-308.
    3. Eltawil, Mohamed A. & Zhengming, Zhao & Yuan, Liqiang, 2009. "A review of renewable energy technologies integrated with desalination systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2245-2262, December.

  92. Berkes, István & Horváth, Lajos, 1996. "Between local and global logarithmic averages," Statistics & Probability Letters, Elsevier, vol. 30(4), pages 369-378, November.

    Cited by:

    1. István Berkes & Lajos Horváth, 1999. "Limit Theorems for Logarithmic Averages of Fractional Brownian Motions," Journal of Theoretical Probability, Springer, vol. 12(4), pages 985-1009, October.

  93. Gombay, Edit & Horváth, Lajos, 1996. "On the Rate of Approximations for Maximum Likelihood Tests in Change-Point Models," Journal of Multivariate Analysis, Elsevier, vol. 56(1), pages 120-152, January.

    Cited by:

    1. Sandip Sinharay, 2017. "Some Remarks on Applications of Tests for Detecting A Change Point to Psychometric Problems," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1149-1161, December.
    2. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    3. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    4. Hans Manner & Bertrand Candelon, 2010. "Testing For Asset Market Linkages: A New Approach Based On Time‐Varying Copulas," Pacific Economic Review, Wiley Blackwell, vol. 15(3), pages 364-384, August.
    5. Antoch, Jaromír & Husková, Marie, 2001. "Permutation tests in change point analysis," Statistics & Probability Letters, Elsevier, vol. 53(1), pages 37-46, May.
    6. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    7. Leonid Torgovitski, 2015. "A Darling–Erdős-type CUSUM-procedure for functional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(1), pages 1-27, January.
    8. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.
    9. Stergios B. Fotopoulos & Alex Paparas & Venkata K. Jandhyala, 2022. "Change point detection and estimation methods under gamma series of observations," Statistical Papers, Springer, vol. 63(3), pages 723-754, June.
    10. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00368334, HAL.
    11. Dominique Guegan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00189141, HAL.
    12. A. Batsidis & N. Martín & L. Pardo & K. Zografos, 2016. "ϕ-Divergence Based Procedure for Parametric Change-Point Problems," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 21-35, March.
    13. Dominique Guegan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00189141, HAL.
    14. Sandip Sinharay, 2016. "Person Fit Analysis in Computerized Adaptive Testing Using Tests for a Change Point," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 521-549, October.
    15. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," PSE-Ecole d'économie de Paris (Postprint) halshs-00368334, HAL.
    16. Batsidis, A. & Horváth, L. & Martín, N. & Pardo, L. & Zografos, K., 2013. "Change-point detection in multinomial data using phi-divergence test statistics," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 53-66.

  94. Horvath, Lajos & Khoshnevisan, Davar, 1995. "Weight functions and pathwise local central limit theorems," Stochastic Processes and their Applications, Elsevier, vol. 59(1), pages 105-123, September.

    Cited by:

    1. Berkes, István & Horváth, Lajos, 1996. "Between local and global logarithmic averages," Statistics & Probability Letters, Elsevier, vol. 30(4), pages 369-378, November.
    2. Fahrner, I. & Stadtmüller, U., 1998. "On almost sure max-limit theorems," Statistics & Probability Letters, Elsevier, vol. 37(3), pages 229-236, March.
    3. Berkes, István & Csáki, Endre, 2001. "A universal result in almost sure central limit theory," Stochastic Processes and their Applications, Elsevier, vol. 94(1), pages 105-134, July.
    4. Berkes, István, 2001. "The law of large numbers with exceptional sets," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 431-438, December.
    5. Berkes, István & Horváth, Lajos & Khoshnevisan, Davar, 1998. "Logarithmic averages of stable random variables are asymptotically normal," Stochastic Processes and their Applications, Elsevier, vol. 77(1), pages 35-51, September.
    6. Csáki, Endre & Földes, Antónia, 1997. "On the logarithmic average of iterated processes," Statistics & Probability Letters, Elsevier, vol. 33(4), pages 347-358, May.

  95. Gombay, Edit & Horváth, Lajos, 1994. "Limit theorems for change in linear regression," Journal of Multivariate Analysis, Elsevier, vol. 48(1), pages 43-69, January.

    Cited by:

    1. Lajos Horváth & Curtis Miller & Gregory Rice, 2021. "Detecting early or late changes in linear models with heteroscedastic errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 577-609, June.
    2. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    3. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    4. Aurelio Fernández Bariviera & M. Belén Guercio & Lisana B. Martinez, 2014. "Informational Efficiency in Distressed Markets: The Case of European Corporate Bonds," The Economic and Social Review, Economic and Social Studies, vol. 45(3), pages 349-369.
    5. Habibi Reza, 2011. "A note on approximating distribution functions of cusum and cusumsq tests," Monte Carlo Methods and Applications, De Gruyter, vol. 17(1), pages 1-10, January.
    6. Lajos Horváth & William Pouliot & Shixuan Wang, 2017. "Detecting at-Most-m Changes in Linear Regression Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 552-590, July.
    7. Jushan, Bai, 1995. "Estimation of multiple-regime regressions with least absolutes deviation," MPRA Paper 32916, University Library of Munich, Germany, revised Feb 1998.
    8. 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).
    9. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

  96. Gombay, Edit & Horváth, Lajos, 1994. "An application of the maximum likelihood test to the change-point problem," Stochastic Processes and their Applications, Elsevier, vol. 50(1), pages 161-171, March.

    Cited by:

    1. 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.
    2. Chioneso S. Marange & Yongsong Qin & Raymond T. Chiruka & Jesca M. Batidzirai, 2023. "A Blockwise Empirical Likelihood Test for Gaussianity in Stationary Autoregressive Processes," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    3. Jandhyala, Venkata K. & Fotopoulos, Stergios B. & Hawkins, Douglas M., 2002. "Detection and estimation of abrupt changes in the variability of a process," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 1-19, July.
    4. Górecki, Tomasz & Horváth, Lajos & Kokoszka, Piotr, 2018. "Change point detection in heteroscedastic time series," Econometrics and Statistics, Elsevier, vol. 7(C), pages 63-88.
    5. Zou, Changliang & Liu, Yukun & Qin, Peng & Wang, Zhaojun, 2007. "Empirical likelihood ratio test for the change-point problem," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 374-382, February.
    6. Horvàth, Lajos & Shao, Qi-Man, 1996. "Darling-Erdos-type theorems for sums of Gaussian variables with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 63(1), pages 117-137, October.
    7. Albert Vexler & Chengqing Wu, 2009. "An Optimal Retrospective Change Point Detection Policy," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 542-558, September.
    8. Gombay, Edit & Serban, Daniel, 2009. "Monitoring parameter change in time series models," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 715-725, April.

  97. Horváth, Lajos, 1993. "Change in autoregressive processes," Stochastic Processes and their Applications, Elsevier, vol. 44(2), pages 221-242, February.

    Cited by:

    1. Park, Chul Gyu & Shin, Dong Wan, 1996. "On the asymptotics of residuals in autoregressive moving average processes with one autoregressive unit root," Statistics & Probability Letters, Elsevier, vol. 27(4), pages 341-346, May.

  98. Csörgo, Miklós & Horváth, Lajos & Shao, Qi-Man, 1993. "Convergence of integrals of uniform empirical and quantile processes," Stochastic Processes and their Applications, Elsevier, vol. 45(2), pages 283-294, April.

    Cited by:

    1. Wenbo V. Li & Qi-Man Shao, 1999. "Small Ball Estimates for Gaussian Processes under Sobolev Type Norms," Journal of Theoretical Probability, Springer, vol. 12(3), pages 699-720, July.
    2. Côté, Marie-Pier & Genest, Christian & Omelka, Marek, 2019. "Rank-based inference tools for copula regression, with property and casualty insurance applications," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 1-15.
    3. Eustasio Barrio & Juan Cuesta-Albertos & Carlos Matrán & Sándor Csörgö & Carles Cuadras & Tertius Wet & Evarist Giné & Richard Lockhart & Axel Munk & Winfried Stute, 2000. "Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 1-96, June.
    4. Qi-Man Shao, 2000. "A Comparison Theorem on Moment Inequalities Between Negatively Associated and Independent Random Variables," Journal of Theoretical Probability, Springer, vol. 13(2), pages 343-356, April.

  99. Horváth, Lajos, 1989. "The limit distributions of likelihood ratio and cumulative sum tests for a change in a binomial probability," Journal of Multivariate Analysis, Elsevier, vol. 31(1), pages 148-159, October.

    Cited by:

    1. Baron, Michael & Rukhin, Andrew L., 2001. "Perpetuities and asymptotic change-point analysis," Statistics & Probability Letters, Elsevier, vol. 55(1), pages 29-38, November.
    2. Verma Vivek & Nath Dilip C., 2019. "Characterization Of The Sum Of Binomial Random Variables Under Ranked Set Sampling," Statistics in Transition New Series, Statistics Poland, vol. 20(3), pages 1-29, September.

  100. Csörgo, Miklós & Horváth, Lajos, 1988. "Invariance principles for changepoint problems," Journal of Multivariate Analysis, Elsevier, vol. 27(1), pages 151-168, October.

    Cited by:

    1. Gombay, Edit, 2001. "U-Statistics for Change under Alternatives," Journal of Multivariate Analysis, Elsevier, vol. 78(1), pages 139-158, July.
    2. Zhang, Hanqin, 2000. "On a weighted embedding for generalized pontograms," Stochastic Processes and their Applications, Elsevier, vol. 88(2), pages 213-224, August.
    3. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.
    4. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.

  101. Horváth, Lajos & Yandell, Brian S., 1988. "Asymptotics of conditional empirical processes," Journal of Multivariate Analysis, Elsevier, vol. 26(2), pages 184-206, August.

    Cited by:

    1. Jürgen Franke & Peter Mwita & Weining Wang, 2015. "Nonparametric estimates for conditional quantiles of time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 107-130, January.
    2. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    3. Salim Bouzebda & Youssouf Souddi & Fethi Madani, 2024. "Weak Convergence of the Conditional Set-Indexed Empirical Process for Missing at Random Functional Ergodic Data," Mathematics, MDPI, vol. 12(3), pages 1-22, January.
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  103. Csörgóo, Miklós & Horváth, Lajos, 1986. "Approximations of weighted empirical and quantile processes," Statistics & Probability Letters, Elsevier, vol. 4(6), pages 275-280, October.

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    1. Lan Xue & Jing Wang, 2010. "Distribution function estimation by constrained polynomial spline regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 443-457.
    2. de Haan, L. & Pereira, T. Themido, 1999. "Estimating the index of a stable distribution," Statistics & Probability Letters, Elsevier, vol. 41(1), pages 39-55, January.
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  104. Aly, Emad-Eldin A. A. & Csörgo, Miklós & Horváth, Lajos, 1985. "Strong approximations of the quantile process of the product-limit estimator," Journal of Multivariate Analysis, Elsevier, vol. 16(2), pages 185-210, April.

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    1. Szeman Tse, 2005. "Quantile process for left truncated and right censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(1), pages 61-69, March.
    2. J. Ghorai, 1991. "Estimation of a smooth quantile function under the proportional hazards model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(4), pages 747-760, December.
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    4. Janssen, Paul & Swanepoel, Jan & Veraverbeke, Noël, 2002. "The modified bootstrap error process for Kaplan-Meier quantiles," Statistics & Probability Letters, Elsevier, vol. 58(1), pages 31-39, May.

  105. Horváth Lajos, 1985. "Estimation From A Length-Biased Distribution," Statistics & Risk Modeling, De Gruyter, vol. 3(1-2), pages 91-114, February.

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  106. Horváth, Lajos, 1984. "Strong approximation of renewal processes," Stochastic Processes and their Applications, Elsevier, vol. 18(1), pages 127-138, September.

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    2. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
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