【书名】An Introduction to State Space Time Series Analysis
【作者】Jacques J. F. Commandeur
              Siem Jan Koopman
【出版社】Oxford University Press

【出版日期】First published 2007

【文件格式】PDF

【文件大小】2.64M

【页数】189

【ISBN出版号】978–0–19–922887–4

【资料类别】Practical Econometrics

【市面定价】无

【扫描版还是影印版】清晰版

【是否缺页】否

【关键词】计量经济学 状态空间模型
Contents
List of Figures x
List of Tables xiv
1. Introduction 1
2. The local level model 9
2.1. Deterministic level 10
2.2. Stochastic level 15
2.3. The local level model and Norwegian fatalities 18
3. The local linear trend model 21
3.1. Deterministic level and slope 21
3.2. Stochastic level and slope 23
3.3. Stochastic level and deterministic slope 26
3.4. The local linear trend model and Finnish fatalities 28
4. The local level model with seasonal 32
4.1. Deterministic level and seasonal 34
4.2. Stochastic level and seasonal 38
4.3. Stochastic level and deterministic seasonal 42
4.4. The local level and seasonal model and UK inflation 43
5. The local level model with explanatory variable 47
5.1. Deterministic level and explanatory variable 48
5.2. Stochastic level and explanatory variable 52
6. The local level model with intervention variable 55
6.1. Deterministic level and intervention variable 56
6.2. Stochastic level and intervention variable 59
7. The UK seat belt and inflation models 62
7.1. Deterministic level and seasonal 63
7.2. Stochastic level and seasonal 64
7.3. Stochastic level and deterministic seasonal 67
7.4. The UK inflation model 70
8. General treatment of univariate state space models 73
8.1. State space representation of univariate models? 73
8.2. Incorporating regression effects? 78
8.3. Confidence intervals 81
8.4. Filtering and prediction 84
8.5. Diagnostic tests 90
8.6. Forecasting 96
8.7. Missing observations 103
9. Multivariate time series analysis? 107
9.1. State space representation of multivariate models 107
9.2. Multivariate trend model with regression effects 108
9.3. Common levels and slopes 111
9.4. An illustration of multivariate state space analysis 113
10. State space and Box–Jenkins methods for time series analysis 122
10.1. Stationary processes and related concepts 122
10.1.1. Stationary process 122
10.1.2. Random process 123
10.1.3. Moving average process 125
10.1.4. Autoregressive process 126
10.1.5. Autoregressive moving average process 128
10.2. Non-stationary ARIMA models 129
10.3. Unobserved components and ARIMA 132
10.4. State space versus ARIMA approaches 133
11. State space modelling in practice 135
11.1. The STAMP program and SsfPack 135
11.2. State space representation in SsfPack? 136
11.3. Incorporating regression and intervention effects? 139
11.4. Estimation of a model in SsfPack? 142
11.4.1. Likelihood evaluation using SsfLikEx 144
11.4.2. The score vector 146
11.4.3. Numerical maximisation of likelihood in Ox 149
11.4.4. The EM algorithm 150
11.4.5. Some illustrations in Ox 151
11.5. Prediction, filtering, and smoothing? 154
12. Conclusions 157
12.1. Further reading 159
APPENDIX A. UK drivers KSI and petrol price 162
APPENDIX B. Road traffic fatalities in Norway and Finland 164
APPENDIX C. UK front and rear seat passengers KSI 165
APPENDIX D. UK price changes 167
Bibliography 171
Index 173