A New Approach to Drawing States in State Space Models
We introduce a new method for drawing state variables in Gaussian state space models from their conditional distribution given parameters and observations. Unlike standard methods, our method does not involve Kalman filtering. We show that for some important cases, our method is computationally more efficient than standard methods in the literature. We consider two applications of our method.
|Date of creation:||2007|
|Date of revision:|
|Contact details of provider:|| Postal: CP 6128, Succ. Centre-Ville, Montréal, Québec, H3C 3J7|
Phone: (514) 343-6540
Fax: (514) 343-5831
Web page: http://www.sceco.umontreal.ca
More information through EDIRC
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.:
- J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
When requesting a correction, please mention this item's handle: RePEc:mtl:montde:2007-06. 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: (Sharon BREWER)
If references are entirely missing, you can add them using this form.