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Temporal Aggregation in Dynamic Linear Models
Author(s) -
Schmidt Alexandra Mello,
Gamerman Dani
Publication year - 1997
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199709)16:5<293::aid-for662>3.0.co;2-q
Subject(s) - series (stratigraphy) , sampling (signal processing) , interval (graph theory) , bayesian probability , linear model , computer science , econometrics , time series , statistics , mathematics , paleontology , filter (signal processing) , combinatorics , computer vision , biology
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, defining a new time series Z k =Σ r i =1 Y rk + i . The relevant factors explaining the variation of Z k can, and in general will, be different, 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 different DLM. In the examples, the industrial production of Brazil is analysed under various aggregation periods and the results are compared. © 1997 John Wiley & Sons, Ltd.