Time Series Analysis by State Space Methods
Seiten
2012
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2nd Revised edition
Oxford University Press (Verlag)
978-0-19-964117-8 (ISBN)
Oxford University Press (Verlag)
978-0-19-964117-8 (ISBN)
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.
Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.
Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.
Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.
Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.
The late James Durbin was Professor of Statistics at the London School of Economics, President of the Royal Statistical Society and President of the International Statistical Institute. He was awarded the society's bronze, silver and gold medals for his contribution to statistics. He was a fellow of the British Academy. Siem Jan Koopman has been Professor of Econometrics at the Free University in Amsterdam and research fellow at the Tinbergen Institute since 1999. He fullfills editorial duties at the Journal of Applied Econometrics, the Journal of Forecasting, the Journal of Multivariate Analysis and Statistica Sinica.
PART I: THE LINEAR STATE SPACE MODEL; PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS
| Erscheint lt. Verlag | 3.5.2012 |
|---|---|
| Reihe/Serie | Oxford Statistical Science Series ; 38 |
| Zusatzinfo | 34 b/w illustrations |
| Verlagsort | Oxford |
| Sprache | englisch |
| Maße | 161 x 235 mm |
| Gewicht | 680 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 0-19-964117-X / 019964117X |
| ISBN-13 | 978-0-19-964117-8 / 9780199641178 |
| Zustand | Neuware |
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