<p>【书名】An Introduction to State Space Time Series Analysis<br/>【作者】Jacques J. F. Commandeur<br/> Siem Jan Koopman<br/>【出版社】Oxford University Press</p><p>【出版日期】First published 2007</p><p>【文件格式】PDF</p><p>【文件大小】2.64M</p><p>【页数】189</p><p>【ISBN出版号】978–0–19–922887–4</p><p>【资料类别】Practical Econometrics</p><p>【市面定价】无</p><p>【扫描版还是影印版】清晰版</p><p>【是否缺页】否</p><p>【关键词】计量经济学 状态空间模型<br/>Contents<br/>List of Figures x<br/>List of Tables xiv<br/>1. Introduction 1<br/>2. The local level model 9<br/>2.1. Deterministic level 10<br/>2.2. Stochastic level 15<br/>2.3. The local level model and Norwegian fatalities 18<br/>3. The local linear trend model 21<br/>3.1. Deterministic level and slope 21<br/>3.2. Stochastic level and slope 23<br/>3.3. Stochastic level and deterministic slope 26<br/>3.4. The local linear trend model and Finnish fatalities 28<br/>4. The local level model with seasonal 32<br/>4.1. Deterministic level and seasonal 34<br/>4.2. Stochastic level and seasonal 38<br/>4.3. Stochastic level and deterministic seasonal 42<br/>4.4. The local level and seasonal model and UK inflation 43<br/>5. The local level model with explanatory variable 47<br/>5.1. Deterministic level and explanatory variable 48<br/>5.2. Stochastic level and explanatory variable 52<br/>6. The local level model with intervention variable 55<br/>6.1. Deterministic level and intervention variable 56<br/>6.2. Stochastic level and intervention variable 59<br/>7. The UK seat belt and inflation models 62<br/>7.1. Deterministic level and seasonal 63<br/>7.2. Stochastic level and seasonal 64<br/>7.3. Stochastic level and deterministic seasonal 67<br/>7.4. The UK inflation model 70<br/>8. General treatment of univariate state space models 73<br/>8.1. State space representation of univariate models? 73<br/>8.2. Incorporating regression effects? 78<br/>8.3. Confidence intervals 81<br/>8.4. Filtering and prediction 84<br/>8.5. Diagnostic tests 90<br/>8.6. Forecasting 96<br/>8.7. Missing observations 103<br/>9. Multivariate time series analysis? 107<br/>9.1. State space representation of multivariate models 107<br/>9.2. Multivariate trend model with regression effects 108<br/>9.3. Common levels and slopes 111<br/>9.4. An illustration of multivariate state space analysis 113<br/>10. State space and Box–Jenkins methods for time series analysis 122<br/>10.1. Stationary processes and related concepts 122<br/>10.1.1. Stationary process 122<br/>10.1.2. Random process 123<br/>10.1.3. Moving average process 125<br/>10.1.4. Autoregressive process 126<br/>10.1.5. Autoregressive moving average process 128<br/>10.2. Non-stationary ARIMA models 129<br/>10.3. Unobserved components and ARIMA 132<br/>10.4. State space versus ARIMA approaches 133<br/>11. State space modelling in practice 135<br/>11.1. The STAMP program and SsfPack 135<br/>11.2. State space representation in SsfPack? 136<br/>11.3. Incorporating regression and intervention effects? 139<br/>11.4. Estimation of a model in SsfPack? 142<br/>11.4.1. Likelihood evaluation using SsfLikEx 144<br/>11.4.2. The score vector 146<br/>11.4.3. Numerical maximisation of likelihood in Ox 149<br/>11.4.4. The EM algorithm 150<br/>11.4.5. Some illustrations in Ox 151<br/>11.5. Prediction, filtering, and smoothing? 154<br/>12. Conclusions 157<br/>12.1. Further reading 159<br/>APPENDIX A. UK drivers KSI and petrol price 162<br/>APPENDIX B. Road traffic fatalities in Norway and Finland 164<br/>APPENDIX C. UK front and rear seat passengers KSI 165<br/>APPENDIX D. UK price changes 167<br/>Bibliography 171<br/>Index 173<br/>ix</p><p>
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