Working Paper: CEPR ID: DP6517
Authors: Michael J. Artis; Jose Garcia Clavel; Mathias Hoffmann; Dilip M. Nachane
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: autoregressive methods; data snooping; dynamic harmonic regression; eigenvalue methods; mixed spectrum; multiple forecast comparisons
JEL Codes: C22; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Harmonic regression methods (C29) | forecasting accuracy (C53) |
| Priestley-Bhansali method (L65) | superior forecasting performance (C53) |
| Truong-Van method (C51) | superior forecasting performance (C53) |
| Dynamic harmonic regression method (C22) | forecasting accuracy (C53) |