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.

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Year of publication:	2007-08
Authors:	McCausland, William J. ; Miller, Shirley ; Pelletier, Denis
Institutions:	Department of Economics, Poole College of Management
Subject:	State space models \| Stochastic volatility \| Count data

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Extent:	application/pdf
Series:	Working Paper Series.
Type of publication:	Book / Working Paper
Notes:	First draft 2007-08 Number 014 27 pages
Classification:	C11 - Bayesian Analysis ; C13 - Estimation ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models ; C63 - Computational Techniques
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005273208