Bayesian Forecasting and Dynamic Models (2nd Edition) by Mike West, Jeff Harrison

By Mike West, Jeff Harrison

The second one version of this publication contains revised, up-to-date, and extra fabric at the constitution, concept, and alertness of sessions of dynamic versions in Bayesian time sequence research and forecasting. as well as extensive ranging updates to important fabric, the second one version contains many extra routines and covers new themes on the study and alertness frontiers of Bayesian forecastings.

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Additional info for Bayesian Forecasting and Dynamic Models (2nd Edition) (Springer Series in Statistics)

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Some of the modelling work was based on developments by Muth (1960), Nerlove and Wage (1964), Thiel and Wage (1964), and Whittle (1965), though a major concern was with more general models, with an emphasis on sensitivity to departures from optimal adaptive parameters, partially motivated by unfounded, inexperienced opposition to discounting from certain academics. The monitoring work in that paper was largely based on the backward cusum controller developed initially in 1961 from cusums in quality control and published in Harrison and Davies (1964).

11m0 , so that Y2 is also relatively highly weighted and m0 contributes only 11% of the weight of information incorporated in m2 . 2. 2 in this example. Finally, the 42 2. Introduction to the DLM coefficient of m0 in mt is simply (1 − At )(1 − At−1 ) . . (1 − A1 ), so that m0 contributes only 1% of the information used to calculate m10 , and as t increases the relevance of this subjective prior decays to zero. 2 Intervention to incorporate external information Model closure is unwise: it precludes the use of extra external information, which is essential in coping with exceptional circumstances, and it is just not acceptable in applied dynamic systems.

In addition, they are independent of (µ0 | D0 ). The values of the variance sequences Vt and Wt may be unknown, but the constant λ and relevant values of the sequence Ft are known. 4. An archetype statistical model assumes that observations are independent and identically normally distributed, denoted by (Yt |µ) ∼ N[µ, V ], (t = 1, 2, . . ), This is the trivial DLM {1, 1, V, 0}. Changes over time in the mean, and sometimes the variance, of this model may be unavoidable features when observations are made on a process or system that is itself continually evolving.

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