An ARIMA-Model-Based Approach to Seasonal Adjustment

This article proposes a model-based procedure to decompose a time series uniquely into mutually independent additive seasonal, trend, and irregular noise components. The series is assumed to follow the Gaussian ARIMA model. Properties of the procedure are discussed and an actual example is given.

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Bibliographic Details
Published in:Journal of the American Statistical Association 1982-03, Vol.77 (377), p.63-70
Main Authors: Hillmer, S. C., Tiao, G. C.
Format: Article
Language:English
Subjects:
ARIMA model
Canonical decomposition
Census X-11 program
Economic models
Economic trends
Model-based decomposition
Polynomials
Pseudospectral density function
Seasonal adjustment
Statistical discrepancies
Stochastic models
Theory and Methods
Time series
Time series models
Unemployment
White noise
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Description
Summary:This article proposes a model-based procedure to decompose a time series uniquely into mutually independent additive seasonal, trend, and irregular noise components. The series is assumed to follow the Gaussian ARIMA model. Properties of the procedure are discussed and an actual example is given.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1982.10477767