Analyzing Strongly Periodic Series in the Frequency Domain: A Comparison of Alternative Approaches with Applications
Michael Artis,
Dilip M Nachane,
Mathias Hoffmann and
Jose Clavel
No 6517, CEPR Discussion Papers from C.E.P.R. Discussion Papers
Abstract:
Strongly periodic series occur frequently in many disciplines. This paper reviews one specific approach to analyzing such series viz. the harmonic regression approach. In this paper, the five major methods suggested under this approach are critically reviewed and compared, and their empirical potential highlighted via two applications. The out-of-sample forecast comparisons are made using the Superior Predictive Ability test, which specifically guards against the perils of data snooping. Certain tentative conclusions are drawn regarding the relative forecasting ability of the different methods.
Keywords: Mixed spectrum; Autoregressive methods; Eigenvalue methods; Dynamic harmonic regression; Data snooping; Multiple forecast comparisons (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Date: 2007-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://cepr.org/publications/DP6517 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:6517
Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP6517
Access Statistics for this paper
More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().