Overcoming Nonadmissibility in ARIMA-Model-Based Signal Extraction.
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to draw a comparison with the results yielded by the AMB decomposition of an approximated model by using an ad hoc filter such as X11-ARIMA and by direct specification of the structural time series models.
Year of publication: |
2001
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Authors: | Fiorentini, Gabriele ; Planas, Christophe |
Published in: |
Journal of Business & Economic Statistics. - American Statistical Association. - Vol. 19.2001, 4, p. 455-64
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Publisher: |
American Statistical Association |
Saved in:
Saved in favorites
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