IDEAS home Printed from https://ideas.repec.org/e/pts113.html
   My authors  Follow this author

Henghsiu Tsai

Personal Details

First Name:Henghsiu
Middle Name:
Last Name:Tsai
Suffix:
RePEc Short-ID:pts113
http://www.stat.sinica.edu.tw/htsai/

Research output

as
Jump to: Articles Chapters

Articles

  1. Yu, Ting-Hung & Tsai, Henghsiu & Rachinger, Heiko, 2020. "Approximate maximum likelihood estimation of a threshold diffusion process," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
  2. Henghsiu Tsai & Heiko Rachinger & Edward M.H. Lin, 2015. "Inference of Seasonal Long-memory Time Series with Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 137-154, March.
  3. Hassler Uwe & Tsai Henghsiu, 2013. "Asymptotic Behavior of Temporal Aggregates in the Frequency Domain," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 47-60, January.
  4. Tsai, Henghsiu & Tsay, Ruey S., 2010. "Constrained Factor Models," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1593-1605.
  5. Tsai, Henghsiu & Chan, Kung-Sik, 2008. "A Note On Inequality Constraints In The Garch Model," Econometric Theory, Cambridge University Press, vol. 24(3), pages 823-828, June.
  6. Henghsiu Tsai & K. S. Chan, 2007. "A Note on Non‐Negative Arma Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 350-360, May.
  7. Henghsiu Tsai & K. S. Chan, 2005. "Maximum likelihood estimation of linear continuous time long memory processes with discrete time data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 703-716, November.
  8. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary and Non‐stationary Continuous‐Time Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(4), pages 583-597, December.
  9. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary And Nonstationary Discrete‐Time Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 613-624, July.
  10. Henghsiu Tsai & K. S. Chan, 2005. "A note on non‐negative continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(4), pages 589-597, September.
  11. Henghsiu Tsai & K. S. Chan, 2005. "Quasi‐Maximum Likelihood Estimation for a Class of Continuous‐time Long‐memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 691-713, September.
  12. Henghsiu Tsai, 2002. "A note on testing for nonlinearity with partially observed time series," Biometrika, Biometrika Trust, vol. 89(1), pages 245-250, March.

Chapters

  1. Henghsiu Tsai & Hwai-Chung Ho & Hung-Yin Chen, 2020. "Non-Parametric Inference on Risk Measures for Integrated Returns," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 72, pages 2485-2497, World Scientific Publishing Co. Pte. Ltd..

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.

Articles

  1. Yu, Ting-Hung & Tsai, Henghsiu & Rachinger, Heiko, 2020. "Approximate maximum likelihood estimation of a threshold diffusion process," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).

    Cited by:

    1. Zhao, Zhenwen & Xi, Yuejuan, 2021. "The first passage time on the (reflected) Brownian motion with broken drift hitting a random boundary," Statistics & Probability Letters, Elsevier, vol. 171(C).
    2. Heiko Rachinger & Edward M. H. Lin & Henghsiu Tsai, 2024. "A bootstrap test for threshold effects in a diffusion process," Computational Statistics, Springer, vol. 39(5), pages 2859-2872, July.
    3. Kirkby, J.L. & Nguyen, Dang H. & Nguyen, Duy & Nguyen, Nhu N., 2022. "Maximum likelihood estimation of diffusions by continuous time Markov chain," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).

  2. Henghsiu Tsai & Heiko Rachinger & Edward M.H. Lin, 2015. "Inference of Seasonal Long-memory Time Series with Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 137-154, March.

    Cited by:

    1. Manabu Asai & Shelton Peiris & Michael McAleer & David E. Allen, 2018. "Cointegrated Dynamics for A Generalized Long Memory Process: An Application to Interest Rates," Documentos de Trabajo del ICAE 2018-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Asai, M. & McAleer, M.J. & Peiris, S., 2017. "Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory," Econometric Institute Research Papers EI2017-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Reisen, Valdério Anselmo & Monte, Edson Zambon & da Conceição Franco, Glaura & Sgrancio, Adriano Marcio & Molinares, Fábio Alexander Fajardo & Bondon, Pascal & Ziegelmann, Flávio Augusto & Abraham, Bo, 2018. "Robust estimation of fractional seasonal processes: Modeling and forecasting daily average SO2 concentrations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 27-43.
    5. Asai, M. & Peiris, S. & McAleer, M.J. & Allen, D.E., 2018. "Cointegrated Dynamics for A Generalized Long Memory Process," Econometric Institute Research Papers EI 2018-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

  3. Hassler Uwe & Tsai Henghsiu, 2013. "Asymptotic Behavior of Temporal Aggregates in the Frequency Domain," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 47-60, January.

    Cited by:

    1. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    2. Cleiton Guollo Taufemback, 2023. "Asymptotic Behavior of Temporal Aggregation in Mixed‐Frequency Datasets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 894-909, August.
    3. Hassler, Uwe, 2014. "Persistence under temporal aggregation and differencing," Economics Letters, Elsevier, vol. 124(2), pages 318-322.
    4. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

  4. Tsai, Henghsiu & Tsay, Ruey S., 2010. "Constrained Factor Models," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1593-1605.

    Cited by:

    1. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
    2. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
    3. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    4. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
    5. Xiang, Jingjie & Li, Kunpeng & Cui, Guowei, 2018. "A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models," Economics Letters, Elsevier, vol. 171(C), pages 144-148.
    6. Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
    7. Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    8. Esther Ruiz & Pilar Poncela, 2022. "Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(2), pages 121-231, November.
    9. Bai, Jushan & Li, Kunpeng & Lu, Lina, 2014. "Estimation and inference of FAVAR models," MPRA Paper 60960, University Library of Munich, Germany.
    10. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    11. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.

  5. Tsai, Henghsiu & Chan, Kung-Sik, 2008. "A Note On Inequality Constraints In The Garch Model," Econometric Theory, Cambridge University Press, vol. 24(3), pages 823-828, June.

    Cited by:

    1. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    2. Nakatani, Tomoaki & Teräsvirta, Timo, 2007. "Positivity Constraints on the Conditional Variances in the Family of Conditional Correlation GARCH Models," SSE/EFI Working Paper Series in Economics and Finance 675, Stockholm School of Economics, revised 14 Feb 2008.
    3. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
    4. Christian Conrad & Menelaos Karanasos, 2008. "Negative Volatility Spillovers in the Unrestricted ECCC-GARCH Model," KOF Working papers 08-189, KOF Swiss Economic Institute, ETH Zurich.
    5. Pan, Qunxing & Li, Peng & Du, Xiuli, 2023. "An improved FIGARCH model with the fractional differencing operator (1-νL)d," Finance Research Letters, Elsevier, vol. 55(PB).
    6. Carl Lönnbark, 2016. "Asymmetry with respect to the memory in stock market volatilities," Empirical Economics, Springer, vol. 50(4), pages 1409-1419, June.
    7. Henghsiu Tsai & K. S. Chan, 2007. "A Note on Non‐Negative Arma Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 350-360, May.
    8. Martínez Ibáñez, Oscar & Olmo, José, 2008. "A nonlinear threshold model for the dependence of extremes of stationary sequences," Working Papers 2072/5361, Universitat Rovira i Virgili, Department of Economics.
    9. Conrad, Christian & Weber, Enzo, 2013. "Measuring Persistence in Volatility Spillovers," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79850, Verein für Socialpolitik / German Economic Association.
    10. Menelaos Karanasos & Ning Zeng, 2013. "Conditional heteroskedasticity in macroeconomics data: UK inflation, output growth and their uncertainties," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 12, pages 266-288, Edward Elgar Publishing.
    11. Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 399-430, August.
    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. Christian Conrad, 2007. "Non-negativity Conditions for the Hyperbolic GARCH Model," KOF Working papers 07-162, KOF Swiss Economic Institute, ETH Zurich.
    14. Abdeljalil Settar & Nadia Idrissi Fatmi & Mohammed Badaoui, 2021. "New Approach in Dealing with the Non-Negativity of the Conditional Variance in the Estimation of GARCH Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(1), pages 55-74, March.

  6. Henghsiu Tsai & K. S. Chan, 2007. "A Note on Non‐Negative Arma Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 350-360, May.

    Cited by:

    1. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    2. Daniel Preve, "undated". "Linear programming-based estimators in nonnegative autoregression," GRU Working Paper Series GRU_2016_001, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    3. Christian Conrad & Menelaos Karanasos, 2008. "Negative Volatility Spillovers in the Unrestricted ECCC-GARCH Model," KOF Working papers 08-189, KOF Swiss Economic Institute, ETH Zurich.
    4. Site Wang & Harsha Gangammanavar & Sandra Ekşioğlu & Scott J. Mason, 2020. "Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization," Annals of Operations Research, Springer, vol. 292(1), pages 371-397, September.

  7. Henghsiu Tsai & K. S. Chan, 2005. "Maximum likelihood estimation of linear continuous time long memory processes with discrete time data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 703-716, November.

    Cited by:

    1. Cheng, Tsung-Lin, 2009. "An efficient algorithm for estimating a change-point," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 559-565, March.
    2. Patrice Abry & Gustavo Didier & Hui Li, 2019. "Two-step wavelet-based estimation for Gaussian mixed fractional processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 157-185, July.
    3. Chiang, Shu-Mei & Chen, Chun-Da & Huang, Chien-Ming, 2019. "Analyzing the impacts of foreign exchange and oil price on biofuel commodity futures," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 37-48.
    4. Simos Theodore, 2012. "On the Exact Discretization of a Continuous Time AR(1) Model driven by either Long Memory or Antipersistent Innovations: A Fractional Algebra Approach," Journal of Time Series Econometrics, De Gruyter, vol. 4(2), pages 1-26, November.
    5. Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
    6. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Chen, Chun-Da & Chiang, Shu-Mei & Huang, Tze-Chin, 2020. "The contagion effects of volatility indices across the U.S. and Europe," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    9. Theodore Simos, 2008. "The exact discrete model of a system of linear stochastic differential equations driven by fractional noise," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1019-1031, November.
    10. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.
    11. Anne Philippe & Caroline Robet & Marie-Claude Viano, 2021. "Random discretization of stationary continuous time processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(3), pages 375-400, April.
    12. Sun, Qi & Xu, Weijun & Xiao, Weilin, 2013. "An empirical estimation for mean-reverting coal prices with long memory," Economic Modelling, Elsevier, vol. 33(C), pages 174-181.

  8. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary and Non‐stationary Continuous‐Time Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(4), pages 583-597, December.

    Cited by:

    1. Man Kasing, 2010. "Extended Fractional Gaussian Noise and Simple ARFIMA Approximations," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-26, September.
    2. Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
    3. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary And Nonstationary Discrete‐Time Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 613-624, July.
    4. Michael A. Thornton & Marcus J. Chambers, 2013. "Continuous-time autoregressive moving average processes in discrete time: representation and embeddability," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 552-561, September.

  9. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary And Nonstationary Discrete‐Time Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 613-624, July.

    Cited by:

    1. Man Kasing, 2010. "Extended Fractional Gaussian Noise and Simple ARFIMA Approximations," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-26, September.
    2. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    3. Chan, Wai-Sum & Chan, Yin-Ting, 2008. "A note on the autocorrelation properties of temporally aggregated Markov switching Gaussian models," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 728-735, April.
    4. Chan, Wai-Sum, 2022. "On temporal aggregation of some nonlinear time-series models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 38-49.
    5. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    6. Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
    7. del Barrio Castro, Tomás & Rachinger, Heiko, 2021. "Aggregation of Seasonal Long-Memory Processes," Econometrics and Statistics, Elsevier, vol. 17(C), pages 95-106.
    8. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    9. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary and Non‐stationary Continuous‐Time Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(4), pages 583-597, December.
    10. Beran, Jan & Schützner, Martin & Ghosh, Sucharita, 2010. "From short to long memory: Aggregation and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2432-2442, November.
    11. Hassler, Uwe, 2014. "Persistence under temporal aggregation and differencing," Economics Letters, Elsevier, vol. 124(2), pages 318-322.
    12. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

  10. Henghsiu Tsai & K. S. Chan, 2005. "A note on non‐negative continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(4), pages 589-597, September.

    Cited by:

    1. Iacus, Stefano M. & Mercuri, Lorenzo & Rroji, Edit, 2017. "COGARCH(p, q): Simulation and Inference with the yuima Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i04).
    2. Creal, Drew D., 2008. "Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2863-2876, February.
    3. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2007. "Putting a Price on Temperature," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 746-767, December.
    4. Benth, Fred Espen & Karbach, Sven, 2023. "Multivariate continuous-time autoregressive moving-average processes on cones," Stochastic Processes and their Applications, Elsevier, vol. 162(C), pages 299-337.
    5. Chunsheng Ma, 2017. "Vector Stochastic Processes with Pólya-Type Correlation Structure," International Statistical Review, International Statistical Institute, vol. 85(2), pages 340-354, August.
    6. P. Brockwell, 2014. "Recent results in the theory and applications of CARMA processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 647-685, August.
    7. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, August.
    8. Mercuri, Lorenzo & Perchiazzo, Andrea & Rroji, Edit, 2024. "A Hawkes model with CARMA(p,q) intensity," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 1-26.

  11. Henghsiu Tsai & K. S. Chan, 2005. "Quasi‐Maximum Likelihood Estimation for a Class of Continuous‐time Long‐memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 691-713, September.

    Cited by:

    1. Patrice Abry & Gustavo Didier & Hui Li, 2019. "Two-step wavelet-based estimation for Gaussian mixed fractional processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 157-185, July.
    2. Chiang, Shu-Mei & Chen, Chun-Da & Huang, Chien-Ming, 2019. "Analyzing the impacts of foreign exchange and oil price on biofuel commodity futures," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 37-48.
    3. Simos Theodore, 2012. "On the Exact Discretization of a Continuous Time AR(1) Model driven by either Long Memory or Antipersistent Innovations: A Fractional Algebra Approach," Journal of Time Series Econometrics, De Gruyter, vol. 4(2), pages 1-26, November.
    4. Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
    5. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Chen, Chun-Da & Chiang, Shu-Mei & Huang, Tze-Chin, 2020. "The contagion effects of volatility indices across the U.S. and Europe," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary and Non‐stationary Continuous‐Time Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(4), pages 583-597, December.
    8. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    9. Theodore Simos, 2008. "The exact discrete model of a system of linear stochastic differential equations driven by fractional noise," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1019-1031, November.
    10. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.
    11. Anne Philippe & Caroline Robet & Marie-Claude Viano, 2021. "Random discretization of stationary continuous time processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(3), pages 375-400, April.
    12. Sun, Qi & Xu, Weijun & Xiao, Weilin, 2013. "An empirical estimation for mean-reverting coal prices with long memory," Economic Modelling, Elsevier, vol. 33(C), pages 174-181.

Chapters

    Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Henghsiu Tsai should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.