UID:
almafu_9959235317302883
Format:
1 online resource (xvi, 554 pages) :
,
digital, PDF file(s).
ISBN:
1-139-88174-4
,
1-107-71301-3
,
1-107-71266-1
,
1-107-04999-7
,
1-107-71590-3
,
1-107-71455-9
,
1-107-72003-6
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
,
Cover; Half Title; TitlePage; Copyright; Contents; List of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1 Introduction; 1.1 The nature of time series; 1.2 Explanatory variables and intervention analysis; 1.3 Multivariate models; 1.4 Statistical treatment; 1.5 Modelling methodology; 1.6 Forecasting; 1.7 Computer software; 2 Univariate time series models; 2.1 Introduction; 2.2 Ad hoc forecasting procedures; 2.3 The structure of time series models; 2.4 Stochastic properties; 2.5 ARIMA models and the reduced form; 2.6 ARIMA modelling; 2.7 Applications
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Exercises3 State space models and the Kalman filter; 3.1 The state space form; 3.2 The Kalman filter; 3.3 Properties of time-invariant models; 3.4 Maximum likelihood estimation and the prediction errordecomposition; 3.5 Prediction; 3.6 Smoothing; 3.7 Non-linearity and non-normality; Appendix. Properties of the multivariate normal distribution; Exercises; 4 Estimation, prediction and smoothing for univariate structuraltime series models; 4.1 Application of the Kalman filter; 4.2 Estimation in the time domain; 4.3 Estimation in the frequency domain; 4.4 Identifiability
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4.5 Properties of estimators4.6 Prediction; 4.7 Estimation of components; Exercises; 5 Testing and model selection; 5.1 Principles of testing; 5.2 Lagrange multiplier tests; 5.3 Tests of specification for structural models; 5.4 Diagnostics; 5.5 Goodness of fit; 5.6 Post-sample predictive testing and model evaluation; 5.7 Strategy for model selection; Exercises; 6 Extensions of the univariate model; 6.1 Trends, detrending and unit roots; 6.2 Seasonality and seasonal adjustment; 6.3 Different timing intervals for the model and observations; 6.4 Data irregularities
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6.5 Time-varyingand non-linear models6.6 Non-normality, count data and qualitative observations; Exercises; 7 Explanatory variables; 7.1 Introduction; 7.2 Estimation in the frequency domain; 7.3 Estimation of models with explanatory variables andstructural time series components; 7.4 Tests and measures of goodness of fit; 7.5 Model selection strategy and applications; 7.6 Intervention analysis; 7.7 Time-varying parameters; 7.8 Instrumental variables; 7.9 Count data; Exercises; 8 Multivariate models; 8.1 Stochastic properties of multivariate models
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8.2 Seemingly unrelated time series equations8.3 Homogeneous systems; 8.4 Testing and model selection; 8.5 Dynamic factor analysis; 8.6 Intervention analysis with control groups; 8.7 Missing observations, delayed observations and contemporaneousaggregation; 8.8 Vector autoregressive models; 8.9 Simultaneous equation models; Exercises; 9 Continuous time; 9.1 Introduction; 9.2 Stock variables; 9.3 Flow variables; 9.4 Multivariate models; Appendix 1 Principal structural time series components and models; Appendix 2 Data sets; A. Energy demand of Other Final Users
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B. US Real Gross National Product
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English
Additional Edition:
ISBN 0-521-40573-4
Additional Edition:
ISBN 0-521-32196-4
Language:
English
URL:
https://doi.org/10.1017/CBO9781107049994
