Overcoming Nonadmissibility in Arima-Model-Based Signal Extraction
Bibliographic Data
| ID | 19419966 |
|---|---|
| Authors | Gabriele Fiorentini (0000-0003-4059-546X, University of Alicante), Christophe Planas |
| Year | 2001 |
| Volume | 19 |
| Issue | 4 |
| Pages | 455-464 |
| Publication date | 2001-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1198/07350010152596709 |
| OpenAlex | W2069769996 |
| Language | EN |
| References cited | 15 |
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
Autoregressive integrated moving average · Econometrics · Statistics · Time series · Blind Source Separation Techniques · Computer Science · Mathematics · Neural Networks and Applications · Time Series Analysis and Forecasting
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| Citation velocity | historical |
|---|---|
| Highly cited | No |