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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010229647 | QA280 H37 1990 | Open Access Book | Book | Searching... |
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Summary
Summary
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
Reviews 1
Choice Review
A well-written book by an author who has made numerous important contributions to the literature of forecasting, time series, and Kalman filters. It is a practical book in the sense that it not only discusses the definitions, interpretations, and analyses of structural time series models, but also illustrates the techniques. Numerical examples illustrate fitting models, testing hypotheses, and making forecasts using time series data sets from economics, sociology, operations research, geography, meteorology, and engineering. Any reader who is familiar with linear algebra and calculus will be able to read and comprehend the material. The emphasis is on the development, selection, and use of models to be used in practice, rather than on rigorous proofs of statements concerning asymptotic properties. A very adequate bibliography directs the interested reader to sources for proofs. A computer program that carries out most of the detailed calculations is available from Harvey. This book has a place in any good undergraduate statistics and econometrics library collection. -F. Giesbrecht, North Carolina State University
Table of Contents
| List of figures |
| Acknowledgement |
| Preface |
| Notation and conventions |
| List of abbreviations |
| 1 Introduction |
| 2 Univariate time series models |
| 3 State space models and the Kalman filter |
| 4 Estimation, prediction and smoothing for univariate structural time series models |
| 5 Testing and model selection |
| 6 Extensions of the univariate model |
| 7 Explanatory variables |
| 8 Multivariate models |
| 9 Continuous time |
| Appendices |
| Selected answers to exercises |
| References |
| Author index |
| Subject index |
