http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Forecasting, structural time series models, and the Kalman filter
1 online resource ( xvi, 554 pages) : illustrations.
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.
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.
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.
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.
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.
Includes bibliographical references (pages 529-542) and indexes.