IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-11760-2_6.html
   My bibliography  Save this book chapter

State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood

In: Longitudinal Research with Latent Variables

Author

Listed:
  • Jacques J. F. Commandeur

    (VU University Amsterdam, Department of Econometrics)

  • Siem Jan Koopman

  • Kees van Montfort

Abstract

We review Kalman filter and related smoothing methods for the latent trajectory in multivariate time series. The latent effects in the model are modelled as vector unobserved components for which we assume particular dynamic stochastic processes. The parameters in the resulting multivariate unobserved components time series models will be estimated by maximum likelihood methods. Some essential details of the state space methodology are discussed in this chapter. An application in the modelling of traffic safety data is presented to illustrate the methodology in practice.

Suggested Citation

  • Jacques J. F. Commandeur & Siem Jan Koopman & Kees van Montfort, 2010. "State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood," Springer Books, in: Kees van Montfort & Johan H.L. Oud & Albert Satorra (ed.), Longitudinal Research with Latent Variables, chapter 0, pages 177-199, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-11760-2_6
    DOI: 10.1007/978-3-642-11760-2_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-642-11760-2_6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.