• 1999 - 2nd Edition, 2nd printing errata
• For those of you with older versions or first edition:
  • 1989 - 1st edition
  • 1997 - 2nd edition, 1st printing errata

The second edition (Springer-Verlag, 1997) 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 addition covers new topics at the research and application frontiers of Bayesian forecasting.

Particular additions include new theory and methodology associated with dynamic linear model analysis; elucidation of the impact of modelling assumptions in DLM analyses, especially in connection 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 autoregressions and time-varying autoregressions; decision analytic approaches to model monitoring and assessment; computation and simulation methods for Bayesian analysis of non-linear models, including, in particular, 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 wide interest to students, researchers and practitioners of time series analysis and forecasting. Readers of the first edition will find useful updates to original sections of the text, as well much new material of relevance to applications in various fields. See chapter headings below. Interested readers might like to look at the related book Applied Bayesian Forecasting and Time Series Analysis by Andy Pole, Mike West and Jeff Harrison This book comes with the BATS software for time series analysis and forecasting.

LIST OF CHAPTERS 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: Basic Analytic And Numerical Approximations Exponential Family Dynamic Models Simulation-Based Methods In Dynamic Models Multivariate Modelling And Forecasting Appendix: Distribution Theory And Linear Algebra