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TEMPORAL AGGREGATION AND SYSTEMATIC-SAMPLING IN STRUCTURAL TIME-SERIES MODELS
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DOI：10.1002/for.3980110403.png)
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Given a structural time-series model specified at a basic time interval, this paper deals with the problems of forecasting efficiency and estimation accuracy generated when the data are collected at a timing interval which is a multiple of the time unit chosen to build the basic model. Results are presented for the simplest structural models, the trend plus error models, under the assumption that the parameters of the model are known. It is shown that the gains in forecasting efficiency and estimation accuracy for having data at finer intervals are considerable for both stock and flow variables with only one exception. No gain in forecasting efficiency is achieved in the case of a stock series that follows a random walk.
Keyword:
SYSTEMATIC SAMPLING
TEMPORAL AGGREGATION
FORECASTING EFFICIENCY
STRUCTURAL MODELS
KALMAN FILTER
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