Synopses & Reviews
The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material in the first edition, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings. These additions include new theory and methodology associated with dynamic linear model analysis; elucidation of the impact of modelling assumptions in DLM analyses, especially in connnection with retrospective time series analysis and model diagnostics; new results on time series decompositions in the state-space framework; developments and applications of state-space auto-regressions and time-varying auto-regressions; decision analytic approaches to model monitoring and assessment; computation and simulation methods for Bayesian analysis of non-linear models, including a new chapter focussed mainly on Markov Chain Monte Carlo approaches in dynamic models; and discussion of new examples and illustrations as well as theory and methods. The text will be of interest to students, researchers, and practitioners of times series analysis and forecasting. Readers of the first edition will find useful updates to original sections of the text, as well as much new material of relevance to applications in various fields.
Synopsis
The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.
Description
Includes bibliographical references (p. [652]-665) and indexes.
Table of Contents
Introduction.- Introduction to the DLM: The first-order polynomial model.- Introduction to the DLM: The regression DLM.- The Dynamic Linear Model.- Univariate Time Series DLM Theory.- Model Specification and Design.- Polynomial Trend Models.- Seasonal Models.- Regression, Autoregression, and Related Models.- Illustrations and Extensions of Standard DLMS.- Intervention and Monitoring.- Multi-Process Models.- Non-Linear Dynamic Models: Analytic and Numerical Approximations.- Exponential Family Dynamic Models.- Simulation-Based Methods in Dynamic Models.- Multivariate Modelling and Forecasting.- Distribution Theory and Linear Algebra. Bibliography.- Author Index.- Subject Index.