DP6517 Analyzing Strongly Periodic Series in the Frequency Domain: A Comparison of Alternative Approaches with Applications

Author(s): Michael J Artis, Jose Garcia Clavel, Mathias Hoffmann, Dilip M Nachane
Publication Date: October 2007
Keyword(s): autoregressive methods, data snooping, dynamic harmonic regression, eigenvalue methods, mixed spectrum, multiple forecast comparisons
JEL(s): C22, C53
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=6517

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.