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Bayesian Forecasting and Dynamic Models
Mike West and Jeff Harrison
Here are two paragraphs that were lost from the 2nd printing at final stages.
Note that we use some basic TeX notation, e.g., "X_t" is "X" subscripted "t", and so forth
Chapter 1, page 31.
The following paragraph should appear at the end of Chapter 1, p31:
- The field continues to develop and flourish. As we approach the new millenium,
we see exciting developments in new fields of application, and increasing
sophistication in modelling developments and advanced computation.
Interested readers might explore some recent studies in Aguilar and West (1998a,b),
Aguilar, Huerta, Prado and West (1999), Cooper and Harrison (1997),
and Prado, Krystal and West (1999), for example.
Readers interested in contacting at least some of the more recently documented
developments, publications and software, can explore the resources and links
on the author web site indicated in the Preface.
Chapter 3
- p92, exercise 6, line -1: the statement C_t^{-1} \to 0 and is incorrect and
should be whited out.
Chapter 10
- p362, Table 10.4, second line under "Forecast:" section
- In the equation for Q_t the term S_{t-1} should be k_t S_{t-1}
Chapter 16
- p601, first bulleted item, second line.
- The equation for E(\Phi) should be
E(\Phi)= S^{-1} (n+q-1)/n
Chapter 17, page 651.
The following paragraph should appear at the end of Chapter 17, p 651:
- As an aside, note the more general Jordan forms for
G matrices of non-observable models that appear in the proof of Theorem 5.2 (the
requisite generalisations of the linear algebraic theory can be found, for example,
in Theorem 8.5 of Nerig (1969).) In such cases
any system matrix with one eigenvalue e of multiplicity n can be reduced to
a form diag(J_{r_1}(e),...,J_{r_m}(e))
with r_1+...+r_m=n and where each J_{r_i}(e)
matrix is a standard Jordan block.
This might even be the diagonal case where each
r_i=1 when G=eI.
This completes the general theory but is of little practical interest.
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