An Introduction to State Space Time Series AnalysisProviding a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level. |
Contents
| 1 | |
| 9 | |
3 The local linear trend model | 21 |
4 The local level model with seasonal | 32 |
5 The local level model with explanatory variable | 47 |
6 The local level model with intervention variable | 55 |
7 The UK seat belt and inflation models | 62 |
8 General treatment of univariate state space models | 73 |
11 State space modelling in practice | 135 |
12 Conclusions | 157 |
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 |
| 171 | |
| 173 | |
9 Multivariate time series analysis | 107 |
10 State space and BoxJenkins methods for time series analysis | 122 |
Other editions - View all
An Introduction to State Space Time Series Analysis Jacques J.F. Commandeur,Siem Jan Koopman Limited preview - 2007 |
An Introduction to State Space Time Series Analysis Jacques J.F. Commandeur,Siem Jan Koopman No preview available - 2007 |
An Introduction to State Space Time Series Analysis Jacques J.F. Commandeur,Siem Jan Koopman No preview available - 2007 |
Common terms and phrases
adding algorithm allowed assumption autocorrelations called Chapter classical regression coefficient confidence consists contains convergence correlogram corresponding deterministic level deterministic seasonal Diagnostic tests discussed effect element equals equation error variance example explanatory variable fatalities Figure filtered forecasts given homoscedasticity identical illustrated important independence indicates inflation intervention variable introduction irregular component lags level and deterministic level and seasonal level disturbances linear trend model log UK drivers log-likelihood function matrix maximum likelihood estimate mean model applied mOmega mPhi mSigma normality number of drivers observation obtained parameters petrol price prediction errors presented random referred residuals respectively satisfied seasonal model Section series analysis shows slope slope component smoothed space methods space models SsfPack standardised statistic stochastic level Table treatment UK drivers KSI unknown variance vector yields zero