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Model Specification and Design

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

Abstract

The mean response function μ t+k of any given model defines, as a function of the step ahead index k, the implied form of the time series and is thus of fundamental interest in model design. The expectation of this, the forecast function f t (k), provides the forecaster’s view of the expected development of the series and we focus on this, rather than the mean response itself, as the central guide to constructing appropriate models. This is purely convention on our part; equivalent discussion could be based on the mean response function instead. We begin with discussion of forecast functions derived from the various TSDLMs of the previous chapter. Together with complementary forms from models for the effects of independent variables, these provide essentially all practically important dynamic linear models.

Keywords

State Vector Discount Factor System Matrix Evolution Variance Variance Matrix 
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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