Providing 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.
1: Introduction 2: The Local Level Model 3: The Local Linear Trend Model 4: The Local Level Model with Seasonal 5: The Local Level Model with Explanatory Variable 6: The Local Level Model with Intervention Variable 7: The UK Seat Belt and Inflation Models 8: General Treatment of Univariate State Space Models 9: Multivariate Time Series Analysis 10: State Space and Box-Jenkins Methods for Time Series Analysis 11: State Space Modelling in Practice 12: Conclusions Appendix A UK Drivers KSI and Petrol Price Appendix B Road Traffic Fatalities in Norway and Finland Appendix C UK Front and Rear Seat Passengers KSI Appendix D UK Price Changes
Jacques J.F. Commandeur is Senior Researcher at the SWOV Institute for Road Safety Research, Leidschendam, The Netherlands. His Ph.D. is from the Department of Psychometrics and Research Methodology of Leiden University. Between 1991 and 2000 he did research for the Department of Data Theory and the Department of Educational Sciences at Leiden University in the fields of multidimensional scaling and nonlinear multivariate data analysis. Since 2000 he has been at SWOV researching the statistical and methodological aspects of road safety research in general, and time series analysis of developments in road safety in particular. His research interests are Procrustes analysis; Multidimensional scaling; Distance-based multivariate analysis; Statistical analysis of time series; Forecasting. He has published in international journals in psychometrics and chemometrics. Siem Jan Koopman is Professor of Econometrics at the Free University Amsterdam and the Tinbergen Institute. His Ph.D. is from the London School of Economics (LSE) and he has held positions at the LSE between 1992 and 1997 and at the CentER (Tilburg University) between 1997 and 1999. In 2002 he visited the US Bureau of the Census in Washington DC as an ASA / NSF / US Census / BLS Research Fellow. His research interests are Statistical analysis of time series; Theoretical and applied time series econometrics; Financial econometrics; Simulation methods; Kalman filtering and smoothing; Forecasting. He has published in many international journals in statistics and econometrics.
Reviews for An Introduction to State Space Time Series Analysis
I really recommend this book. It is a very good read and it is very reasonably priced. Paul Eilers, The Newsletter of the Dutch Classification Society