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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.
- Sales Rank: #1445904 in eBooks
- Published on: 2007-07-19
- Released on: 2007-07-19
- Format: Kindle eBook
Review
a fascinating read...excellent CFA Society of the UK 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
About the Author
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.
Most helpful customer reviews
4 of 4 people found the following review helpful.
A good starting place
By Jordan McBain
I purchased this book based on the recommendations of the previous two reviewers. I was disappointed at first but quickly became pleased. The book is thankfully short (160 pages) and provides a good introduction as to exactly what state space modeling is.
This book should only be purchased as an introduction to state space time series modeling as much detail is missing.
Readers should expect chapters 1-7 will present exemplar scenarios (e.g. economics data driven by an "explanatory variable" like inflation, or ones driven by an explanatory variable with an interference variable (such as the introduction of new tax levels)). The author provides examples of these models throughout the first few chapters - along with a simple mathematical model.
In Chapter 8 and on, the author introduces the state space method of modeling the previously demonstrated scenarios. He takes each of their mathematical models and shows how they can be represented in state-space form (i.e. matrix form).
The book concludes with a "how to" on using software modeling tools to model time-series data. Not much detail/intuition is given as to the nitty gritty of how a state space model can be rectified with the data to be modeled (as the author seems to think the reader will be satisfied with the software package "how to").
I feel empowered to carry on in my exploration of this topic with thicker, more advanced books.
6 of 6 people found the following review helpful.
Great for the technical non-specialist.
By Roy Marsten
I found this book extremely helpful and I highly recommend it. I am a technical person, but in a different area (optimization). I have been trying to learn about forecasting, and particularly state space methods. I have read the classics: Harvey (1989) and Durbin and Koopman (2002) and they are great, but they leave out all the practical stuff. Why are we doing this? What will the results look like? How do we actually initialize the process? Nothing but equations and more equations for more and more obscure mathematical variations. How do we actually use this stuff? That is where Commandeur & Koopman is invaluable. The STAMP and SsfPack computer programs are used as a kind of "Deus ex Machina", so you have to know about the Kalman filtering and BFGS that is going on behind the curtain. But if you get the math but are confused about how to apply it, then this is the book for you.
1 of 4 people found the following review helpful.
There is no much deatils.
By Jinping Shi
The book is more like an ad for their software. You do not learn much from it. There is no much deatils.
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