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ERIC Number: EJ789055
Record Type: Journal
Publication Date: 2008-Jan
Pages: 28
Abstractor: Author
ISSN: ISSN-0027-3171
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
Multivariate Behavioral Research, v43 n1 p1-28 Jan 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can be applied to either nontransformed or differenced series, so the advantages and drawbacks of both procedures were compared. The best results were 79% of correct identifications for SCAN and 80% for ESACF. For some models and parameterizations, the accuracy of SCAN and ESACF was disappointing. The key finding of the study is that both human experts and automated methods provide inconsistent model identifications. Hence an elaborated strategy for model selection combining different techniques was developed and demonstrated on 2 empirical examples. (Contains 4 figures and 12 tables.)
Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A