Advanced Search
MyIDEAS: Login to save this paper or follow this series

Fast Filtering and Smoothing for Multivariate State Space Models

Contents:

Author Info

  • Koopman, S.J.M.
  • Durbin, J.

    (Tilburg University, Center for Economic Research)

Abstract

This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods for multivariate filtering and smoothing. Also, the treatment of the diffuse initial state vector in multivariate models is much simpler than existing methods. The paper presents details of relevant algorithms for filtering, prediction and smoothing. Proofs are provided. Three examples of multivariate models in statistics and economics are presented for which the new approach is particularly relevant.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://arno.uvt.nl/show.cgi?fid=3677
Our checks indicate that this address may not be valid because: 404 Not Found. If this is indeed the case, please notify (Richard Broekman)
Download Restriction: no

Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1998-18.

as in new window
Length:
Date of creation: 1998
Date of revision:
Handle: RePEc:dgr:kubcen:199818

Contact details of provider:
Web page: http://center.uvt.nl

Related research

Keywords: Diffuse initialisation; Kalman filter; multivariate models; smoothing; state space; time series;

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Siem Jan Koopman & N.G. Shephard, 1992. "Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)," STICERD - Econometrics Paper Series /1992/241, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  2. Koopman, S.J.M. & Lai, H.N., 1998. "Modelling bid-ask spreads in competitive dealership markets," Discussion Paper 1998-032, Tilburg University, Center for Economic Research.
  3. Huang, Roger D & Stoll, Hans R, 1997. "The Components of the Bid-Ask Spread: A General Approach," Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 995-1034.
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 in new window

Cited by:
  1. Joao Valle e Azevedo & Siem Jan Koopman & Antonio Rua, 2003. "Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area," Tinbergen Institute Discussion Papers 03-069/4, Tinbergen Institute.
  2. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
  3. T. Berger & G. Everaert, 2006. "Re-examining the Structural and the Persistence Approach to Unemployment," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/383, Ghent University, Faculty of Economics and Business Administration.
  4. M. Dossche & G. Everaert, 2005. "Measuring inflation persistence: a structural time series approach," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/340, Ghent University, Faculty of Economics and Business Administration.
  5. Snyder Ralph D & Forbes Catherine S, 2003. "Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
  6. Borus Jungbacker & Siem Jan Koopman & Michel van der Wel, 0000. "Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates," Tinbergen Institute Discussion Papers 09-041/4, Tinbergen Institute, revised 17 Sep 2010.
  7. Ingvar Strid & Karl Walentin, 2009. "Block Kalman Filtering for Large-Scale DSGE Models," Computational Economics, Society for Computational Economics, vol. 33(3), pages 277-304, April.
  8. Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gian Luigi & Proietti, Tommaso, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:dgr:kubcen:199818. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Richard Broekman).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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