Robust seasonal adjustment by Bayesian modelling

Robust seasonal adjustment by Bayesian modelling

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Article ID: iaor19971700
Country: United Kingdom
Volume: 15
Issue: 5
Start Page Number: 355
End Page Number: 367
Publication Date: Sep 1996
Journal: International Journal of Forecasting
Authors:
Keywords: Bayesian modelling
Abstract:

Akaike’s BAYSEA approach to seasonal decomposition is designed to capture the respective merits of several pre-existing adjustment techniques. BAYSEA is computationally efficient, requires only weak assumptions about the data-generating process, and is based on solid inferential (namely, Bayesian) foundations. This paper presents a model similar to that used in BAYSEA, but based on a double exponential rather than a Gaussian error model. The resulting procedure has the advantages of Akaike’s method, but in addition is resistant to outliers. The optimal decomposition is obtained rapidly using a sparse linear programming code. Confidence bands and predictive intervals can be obtained using Gibbs sampling.

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