Forecasting, Structural Time Series Models and the Kalman Filter [Paperback]

This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose.A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.This book provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the book is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. This technique was originally developed in control engineering but is becoming increasingly important in economics and operations research. The book is primarily concerned with modeling economic and social time series and with addressing the special problems that the treatment of such series pose.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. A well-written book by an author who has made numerous important contributions to the literl#.