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 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.
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.
Inhaltsangabe
1: Introduction Part I: The linear state space model 2: Local level model 3: Linear Gaussian state space models 4: Filtering, smoothing and forecasting 5: Initialisation of Filter and smoother 6: Further computational aspects 7: Maximum likelihood estimation of parameters 8: Illustrations of the use of the linear Gaussian model Part II: Non-Gaussian and nonlinear state space models 9: Special cases of nonlinear and non-Gaussian models 10: Approximate filtering and smoothing 11: Importance sampling for smoothing 12: Particle filtering 13: Bayesian estimation of parameters 14: Non-Gaussian and nonlinear illustrations Subject Index
1: Introduction Part I: The linear state space model 2: Local level model 3: Linear Gaussian state space models 4: Filtering, smoothing and forecasting 5: Initialisation of Filter and smoother 6: Further computational aspects 7: Maximum likelihood estimation of parameters 8: Illustrations of the use of the linear Gaussian model Part II: Non-Gaussian and nonlinear state space models 9: Special cases of nonlinear and non-Gaussian models 10: Approximate filtering and smoothing 11: Importance sampling for smoothing 12: Particle filtering 13: Bayesian estimation of parameters 14: Non-Gaussian and nonlinear illustrations Subject Index
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