Forecasting, Structural Time Series Models and the Kalman Filter

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Paperback | 29 April 1991

At a Glance

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572 Pages

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22.86 x 15.24 x 3.23

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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.

Industry Reviews

'... if you're looking for a state of the art monograph on applied aspects of state-space representations, and Kalman filtering ... then Harvey's book is required reading.' Econometric Theory

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You Can Find This Book In

Non-Fiction Economics Economic Forecasting Econometrics Economic Statistics Mathematics Probability & Statistics

List of figures
Acknowledgement
Preface
Notation and conventions
List of abbreviations
Introduction
Univariate time series models
State space models and the Kalman filter
Estimation, prediction and smoothing for univariate structural time series models
Testing and model selection
Extensions of the univariate model
Explanatory variables
Multivariate models
Continuous time
Appendices
Selected answers to exercises
References
Author index
Subject index
Table of Contents provided by Publisher. All Rights Reserved.

Forecasting, Structural Time Series Models and the Kalman Filter

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

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