## 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. |

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### Contents

2The local level model | 1-9 |

3The local linear trend model | 1-27 |

4The local level model with seasonal | 1-43 |

5The local level model with explanatory variable | 4-17 |

6The local level model with intervention variable | 5-7 |

7The UK seat belt and inflation models | 6-7 |

8General treatment of univariate state space models | 6-22 |

9Multivariate time series analysis | 8-23 |

11State space modelling in practice | 10-16 |

12Conclusions | 10-47 |

APPENDIX AUK drivers KSI and petrol price | 10-54 |

APPENDIX BRoad traffic fatalities in Norway and Finland | 10-59 |

APPENDIX CUK front and rear seat passengers KSI | 10-61 |

APPENDIX DUK price changes | 10-66 |

10-72 | |

10-75 | |

### 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

Akaike information criterion algorithm autocorrelations confidence interval confidence limits control series convergence the value correlogram deterministic level model deterministic seasonal model Diagnostic tests displayed in Figure drivers KSI series EM algorithm estimation error variance explanatory variable log forecasts homoscedasticity hyperparameters irregular component Kalman filter level and deterministic level and intervention level and seasonal level component level disturbances likelihood function linear regression linear trend model log drivers log of petrol log petrol price log-likelihood function maximum likelihood estimate model equals Norwegian fatalities number of drivers number of UK observation disturbances observed time series obtained parameters prediction errors presented pulse intervention variables random process regression analysis regression coefficient road traffic fatalities score vector seasonal component seat belt law Section 4.4 series analysis series models slope component space methods space models SsfPack standardised smoothed stationary process stochastic level model stochastic slope UK drivers KSI variable log petrol variance matrix zero