Time Series Analysis by State Space Methods

by Durbin and Koopman

Oxford University Press, 2001

List Price $110.00, Estima's Price $90.00

This is an excellent choice for RATS users interested in pursuing state space modelling techniques. Many of the enhancements to the DLM instruction that were introduced in Version 6.35 of RATS were developed in the process of writing RATS code for the worked examples from this book (see Durbin and Koopman examples for the RATS programs).

From the publisher’s description:
Providing analyses from both classical and Bayesian perspectives, this book presents a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system.

Contents

1. Introduction
2. Local level model
3. Linear Gaussian state space models
4. Filtering, smoothing and forecasting
5. Initialisation of filter and smoother
6. Further computational aspects
7. Maximum likelihood estimation
8. Bayesian analysis
9. Illustrations of the use of the linear Gaussian model
10. Non-Gaussian and nonlinear state space models
11. Importance sampling
12. Analysis from a classical standpoint
13. Analysis from a Bayesian standpoint
14. Non-Gaussian and nonlinear illustrations

Plus References; Author Index; Subject Index

---

Copyright © 2013 Estima. All Rights Reserved.
This file was last modified on 09/27/12