SsfPack 3.0: Statistical Algorithms for Models in State Space Form

by Siem Jan Koopman, Neil Shephard, Jurgen A. Doornik (2008)

Publisher: Timberlake Consultants Press
ISBN: 978-0-9557076-3-6
Number of pages: 268
Price : £25.00 + p&p


Contents

Table of Contents
Book Order Form

Table of Contents

I Prologue

1. Introduction

1.1 General information
1.2 Overview of the SsfPack book
1.3 New Features
1.4 Support Platforms
1.5 Citation
1.6 World Wide Web
1.7 Acknowledgments

2 The state space form in SsfPack 3

2.1 The state space representation in SsfPack
2.2 Initial conditions
2.3 Time-varying state space form
2.4 Formulating the state space
2.5 Missing values

3 Models in state space form 11

3.1 Autoregressive moving average models
3.2 Autoregressive integrated moving average models
3.3 Seasonal ARIMA models
3.4 Structural time series models
3.5 Regression models
3.6 Adding regression effects to time series models
3.7 Nonparametric cubic spline models

II SsfPack Basic documentation

4 Prediction, smoothing and simulation

4.1 Simulating data from state space models
4.2 The Kalman Filter
4.3 Moment smoothing
4.4 Simulation smoothing
4.5 The conditional density: its mean and simulation

5 Ready-to-use functions

5.1 Likelihood and score evaluation
5.2 Prediction and smoothing
5.3 Applications

6 Illustrations

6.1 Seasonal components
6.2 Combining models
6.3 Regression effects in time-invariant models
6.4 Bayesian parameter estimation

II SsfPack Extended documentation

7 State Space form in SsfPack Extended

7.1 Variance matrices and restrictions
7.2 Initial Condition
7.3 Supporting functions

8 Prediction, filtering, smoothing and simulation

8.1 Simulating date from state space models
8.2 The univariate algorithms
8.3 The multivariate algorithms
8.4 Simulating smoothing

9 More ready-to-use functions

9.1 Exact likelihood evaluation
9.2 Augmentation method for likelihood evaluation
9.3 Regression
9.4 Prediction, filtering and smoothing
9.5 Forecasting
9.6 Weight functions
9.7 Bootstrap for general state space models

More illustrations

10.1 Estimation in multivariate local level model
10.2 Approximations to nonlinear non-Gaussian models

A SARIMA models in state space

A.1 ARIMA model with d = 2
A.2 SARIMA model with d = 1 and D = 1
A.3 SARIMA model with d = 2 and D = 1

References

Author Index

Subject Index