IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v73y2011i1p99-122.html
   My bibliography  Save this article

Modelling non‐homogeneous Poisson processes with almost periodic intensity functions

Author

Listed:
  • Nan Shao
  • Keh‐Shin Lii

Abstract

No abstract is available for this item.

Suggested Citation

  • Nan Shao & Keh‐Shin Lii, 2011. "Modelling non‐homogeneous Poisson processes with almost periodic intensity functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 99-122, January.
  • Handle: RePEc:bla:jorssb:v:73:y:2011:i:1:p:99-122
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-9868.2010.00758.x
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helmers, Roelof & Mangku, I. Wayan & Zitikis, Ricardas, 2005. "Statistical properties of a kernel-type estimator of the intensity function of a cyclic Poisson process," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 1-23, January.
    2. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    3. Helmers, Roelof & Wayan Mangku, I. & Zitikis, Ricardas, 2003. "Consistent estimation of the intensity function of a cyclic Poisson process," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 19-39, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodrigo Saul Gaitan & Keh‐Shin Lii, 2021. "On the Estimation of Periodicity or Almost Periodicity in Inhomogeneous Gamma Point‐Process Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 711-736, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roelof Helmers & I. Mangku, 2009. "Estimating the intensity of a cyclic Poisson process in the presence of linear trend," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 599-628, September.
    2. Roelof Helmers & Qiying Wang & Ričardas Zitikis, 2009. "Confidence regions for the intensity function of a cyclic Poisson process," Statistical Inference for Stochastic Processes, Springer, vol. 12(1), pages 21-36, February.
    3. Roelof Helmers & I. Mangku, 2012. "Predicting a cyclic Poisson process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(6), pages 1261-1279, December.
    4. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    5. 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.
    6. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    7. Gerhard, Frank & Hess, Dieter & Pohlmeier, Winfried, 1998. "What a Difference a Day Makes: On the Common Market Microstructure of Trading Days," CoFE Discussion Papers 98/01, University of Konstanz, Center of Finance and Econometrics (CoFE).
    8. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Hagmann, M. & Scaillet, O., 2007. "Local multiplicative bias correction for asymmetric kernel density estimators," Journal of Econometrics, Elsevier, vol. 141(1), pages 213-249, November.
    10. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    11. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    12. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    13. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    14. Monira Essa Aloud, 2016. "Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 55-64.
    15. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 91-114, March.
    16. Rituparna Sen & Pulkit Mehrotra, 2016. "Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 137-150, June.
    17. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Naushad Mamode Khan, 2019. "Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework," JRFM, MDPI, vol. 12(2), pages 1-13, April.
    18. Dionne, Georges & Pacurar, Maria & Zhou, Xiaozhou, 2015. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 202-219.
    19. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    20. Álvaro Cartea & Thilo Meyer-Brandis, 2010. "How Duration Between Trades of Underlying Securities Affects Option Prices," Review of Finance, European Finance Association, vol. 14(4), pages 749-785.
    21. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," Energy Economics, Elsevier, vol. 40(C), pages 222-232.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:73:y:2011:i:1:p:99-122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may 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.