Block Sampler and Posterior Mode Estimation for a Nonlinear and Non-Gaussian State-Space Model with Correlated Errors

Yasuhiro Omori () and Toshiaki Watanabe
Additional contact information
Toshiaki Watanabe: Faculty of Economics, Tokyo Metropolitan University

No CIRJE-F-221, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: In a linear Gaussian state-space time series analysis, a disturbance smoother and a simula-tion smoother are widely used procedures for smoothing and sampling state or disturbance vectors given observations. Several smoothing procedures are also proposed for a non-Gaussian observation process. However, it is assumed that a state equation is linear and that an observation vector and a state vector are conditionally independent. These as-sumptions often need to be relaxed in the analysis of real data. Thus this article considers a general state-space model with a non-Gaussian observation process and a nonlinear state equation where an observation vector and a state vector are allowed to be dependent. We describe a disturbance smoother and a simulation smoother for such models and give numerical examples using simulated data and real data.

Pages: 40 pages
Date: 2003-05
References: Add references at CitEc
Citations: View citations in EconPapers (3)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-Space Model with Correlated Errors (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2003cf221

Access Statistics for this paper

More papers in CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by CIRJE administrative office ().