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Temporal aggregation and systematic sampling in structural time‐series models
Author(s) -
González Pilar
Publication year - 1992
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/for.3980110403
Subject(s) - series (stratigraphy) , econometrics , time series , computer science , random walk , interval (graph theory) , stock (firearms) , sampling (signal processing) , statistics , mathematics , mechanical engineering , paleontology , filter (signal processing) , combinatorics , engineering , computer vision , biology
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.