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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.
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Product details
Series: Practical Econometrics
Hardcover: 240 pages
Publisher: Oxford University Press; 1 edition (August 30, 2007)
Language: English
ISBN-10: 0199228876
ISBN-13: 978-0199228874
Product Dimensions:
9.3 x 0.7 x 6 inches
Shipping Weight: 15.5 ounces (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#1,130,612 in Books (See Top 100 in Books)
This slim book clearly lays out the connections between classic linear regression and state space models for timeseries data.It begins with simple linear regression, y= a + bx. It then connects that to timeseries data (the x's are ordered and y's may be serially correlated). Then, in successive chapters it shows what happens when you allow individual regression parameters to vary stochastically over time -- alone and in concert. It never loses the connection back to regression as a sort of "deterministic state space model" (where parameters are fixed for all time and do not vary stochastically). Successive chapters evaluate successive generalizations using the same data set, computing the AIC to compare model efficacy with each change, and graphing the unobserved state evolution vs the data for each generalization so that you can see what aspects of the data each stochastic component is able to explain. It sometimes feels repetitive, but is always very clear.What this book does not do is tell you how to solve such models (or, eg, express them as Kalman filters). It relies on software to perform maximum likelihood estimation of the parameters of each state space model (intial values, deterministic parameters, and noise variances). Again the connection back to linear regression is highlighted in the numerical solutions: when all parameters are made deterministic (0 variance), and the MLE is performed, what emerges are the same regression parameters you'd get from a classic least squares fit.As others say, this is definitely an introduction, not a reference -- you likely will not return to it once consumed. But what a fine meal!
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.
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.
This book paved the way for teaching modern state space times series analysis. It is already a classic.
It is a interesting book to understand State-Space models, however there are many typos in the equations (there is a errata at the web). But, the weak part of the book is the use of a routine in Ox. The output of that routine is very poor so you end up thinking that Kalman filter is a black-box. It would be more useful to compare the results (and output) with standard packages such as Stata, R or Gretl!
The book is more like an ad for their software. You do not learn much from it. There is no much deatils.
This is the book that I wish was available when I first began using state space time series models (often called structural time series or unobserved components models). Commandeur and Koopman take you step by step from the most basic Local Level model (Chapter 2) through models with a linear trend (Chapter 3), seasonal effects (Chapter 4), a explanatory variable (Chapter 5),and an intervention variable (Chapter 6). Inexplicably they never put together the components to cover the Basic Structural Model, the most common state space time series model.Chapter 7 goes through the steps of model building for two of the data set covered in the text. Chapter 8 does a nice job putting the model into matrix form, and covering confidence intervals, discussing the filtered, smoothed and predicted states, diagnostic tests, forecasting, and missing observations. I wish that they spent more time on each of these topics.I cannot comment on Chapters 9 through 11 (multivariate time series, state space and Box-Jenkins methods, and state space models using STAMP and SsfPack). The data sets used in the book can be downloaded and used in the software package of your choice.Overall, if you would like to understand state space time series analysis and like learning by example then this is a good place to start.
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