The Dynamic Linear Model

  • Mike West
  • Jeff Harrison
Part of the Springer Series in Statistics book series (SSS)

Abstract

The first-order polynomial and simple regression models of the preceding two chapters illustrate many basic concepts and important features of the general class of Normal Dynamic Linear Models, referred to as Dynamic Linear Models (DLMs) when the normality is understood. This class of models is described and analysed here, providing a basis for the special cases in later chapters and for further generalisations to follow. The principles used by a Bayesian forecaster in structuring forecasting problems, as introduced in Section 1.3 of Chapter 1, are reaffirmed here. The approach of Bayesian forecasting and dynamic modelling comprises, fundamentally,
  1. (i)

    a sequential model definition;

     
  2. (ii)

    structuring using parametric models with meaningful parametrisation;

     
  3. (iii)

    probabilistic representation of information about parameters;

     
  4. (iv)

    forecasts derived as probability distributions.

     

Keywords

State Vector Posterior Distribution Variance Matrix Observational Variance Observation Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Mike West
    • 1
  • Jeff Harrison
    • 2
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA
  2. 2.Department of StatisticsUniversity of WarwickCoventryUK

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