IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v28y1998i2p171-191.html
   My bibliography  Save this article

Smoothing non-Gaussian time series with autoregressive structure

Author

Listed:
  • Grunwald, Gary K.
  • Hyndman, Rob J.

Abstract

No abstract is available for this item.

Suggested Citation

  • Grunwald, Gary K. & Hyndman, Rob J., 1998. "Smoothing non-Gaussian time series with autoregressive structure," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 171-191, August.
    • Handle: RePEc:eee:csdana:v:28:y:1998:i:2:p:171-191
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(98)00034-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hyndman, R.J. & Wand, M.P., "undated". "Nonparametric autocovariance function estimation," Statistics Working Paper _006, Australian Graduate School of Management.
    2. Ruey S. Tsay, 1992. "Model Checking Via Parametric Bootstraps in Time Series Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 1-15, March.
    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. Hyndman, R.J. & Grunwald, G.K., 1999. "Generalized Additive Modelling of Mixed Distribution Markov Models with Application to Melbourne's Rainfall," Monash Econometrics and Business Statistics Working Papers 2/99, Monash University, Department of Econometrics and Business Statistics.

    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. E. E. Ioannidis & G. A. Chronis, 2005. "Extreme Spectra of Var Models and Orders of Near‐Cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(3), pages 399-421, May.
    2. Christian Weiß, 2015. "A Poisson INAR(1) model with serially dependent innovations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(7), pages 829-851, October.
    3. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    4. John P. Miller & Paul Newbold, 1995. "A GENERALIZED VARIANCE RATIO TEST OF ARIMA (p, 1, q) MODEL SPECIFICATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(4), pages 403-413, July.
    5. Newbold, Paul & Leybourne, Stephen & Wohar, Mark E., 2001. "Trend-stationarity, difference-stationarity, or neither: further diagnostic tests with an application to U.S. Real GNP, 1875-1993," Journal of Economics and Business, Elsevier, vol. 53(1), pages 85-102.
    6. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
    7. Christian H. Weiß & Martin H.-J. M. Feld & Naushad Mamode Khan & Yuvraj Sunecher, 2019. "INARMA Modeling of Count Time Series," Stats, MDPI, vol. 2(2), pages 1-37, June.
    8. Vance L. Martin & Andrew R. Tremayne & Robert C. Jung, 2014. "Efficient Method Of Moments Estimators For Integer Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 491-516, November.
    9. Franses, Ph.H.B.F. & Neele, J. & van Dijk, D.J.C., 1998. "Modeling asymmetric volatility in weekly Dutch temperature data," Econometric Institute Research Papers EI 9840, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Zacharias Psaradakis, 1998. "Bootstrap-based evaluation of markov-switching time series models," Econometric Reviews, Taylor & Francis Journals, vol. 17(3), pages 275-288.
    11. Margherita Gerolimetto & Stefano Magrini, 2016. "Distribution Dynamics in the US. A Spatial Perspective," Working Papers 2016:02, Department of Economics, University of Venice "Ca' Foscari".
    12. Robert C. Jung & Andrew R. Tremayne, 2020. "Maximum-Likelihood Estimation in a Special Integer Autoregressive Model," Econometrics, MDPI, vol. 8(2), pages 1-15, June.
    13. Stefano Magrini & Margherita Gerolimetto, 2015. "Spatial Distribution Dynamics," ERSA conference papers ersa15p1172, European Regional Science Association.
    14. Ristić Miroslav M. & Weiß Christian H. & Janjić Ana D., 2016. "A Binomial Integer-Valued ARCH Model," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-21, November.
    15. Cláudia Santos & Isabel Pereira & Manuel G. Scotto, 2021. "On the theory of periodic multivariate INAR processes," Statistical Papers, Springer, vol. 62(3), pages 1291-1348, June.
    16. Adriana Bortoluzzo & Pedro Morettin & Clelia Toloi, 2010. "Time-varying autoregressive conditional duration model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 847-864.
    17. Andreea Röthig & Andreas Röthig & Carl Chiarella, 2015. "On Candlestick-based Trading Rules Profitability Analysis via Parametric Bootstraps and Multivariate Pair-Copula based Models," Research Paper Series 362, Quantitative Finance Research Centre, University of Technology, Sydney.
    18. Yongning Wang & Ruey S. Tsay, 2013. "On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations," Econometrics, MDPI, vol. 1(1), pages 1-31, April.
    19. Jentsch, Carsten & Weiß, Christian, 2017. "Bootstrapping INAR models," Working Papers 17-02, University of Mannheim, Department of Economics.
    20. Nielsen, Henrik Aa. & Madsen, Henrik, 2001. "A generalization of some classical time series tools," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 13-31, July.

    More about this item

    Statistics

    Access and download statistics

    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:eee:csdana:v:28:y:1998:i:2:p:171-191. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.