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Temporal Aggregation in Dynamic Linear Models

✍ Scribed by Alexandra Mello Schmidt; Dani Gamerman

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 202 KB
Volume: 16
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199709)16:5<293::aid-for662>3.0.co;2-q

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✦ Synopsis

One important aspect concerning the analysis and forecasting of time series that is sometimes neglected is the relationship between a model and the sampling interval, in particular, when the observation is cumulative over the sampling period. This paper intends to study the temporal aggregation in Bayesian dynamic linear models (DLM). Suppose that a time series Y t is observed at time units t and the observations of the process are aggregated over r units of time, de®ning a new time series Z k r i 1 Y rk i . The relevant factors explaining the variation of Z k can, and in general will, be dierent, depending on how the sampling interval r is chosen. It is shown that if Y t follows certain dynamic linear models, then the aggregated series can also be described by possibly dierent DLM. In the examples, the industrial production of Brazil is analysed under various aggregation periods and the results are compared. # 1997

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