Data Augmentation and Dynamic Linear Models

Frühwirth-Schnatter, Sylvia (1992) Data Augmentation and Dynamic Linear Models. Forschungsberichte / Institut für Statistik, 28. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

[img]
Preview
PDF
document.pdf

Download (763kB)

Abstract

We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract)

Item Type: Paper
Keywords: Approximate Bayesian Analysis / Data Augmentation / Dynamic Linear Models / Kalman Filtering / Model Likelihood / State Space Models
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 11 Jul 2006 09:07
Last Modified: 22 Oct 2019 00:41
URI: https://epub.wu.ac.at/id/eprint/392

Actions

View Item View Item

Downloads

Downloads per month over past year

View more statistics