Detection of non-linear structure in time series
In this paper we introduce a new method to detect lags in time series by using permutation entropy. The method is applied to several well-known dynamic processes. The good power performance of the new method in detecting memory structure/lags is notable and gives rise to an expectation that it may form a suitable basis for constructive specification searches.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, 09.
- Matilla-Garcia, Mariano & Ruiz Marin, Manuel, 2008. "A non-parametric independence test using permutation entropy," Journal of Econometrics, Elsevier, vol. 144(1), pages 139-155, May.
When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:105:y:2009:i:1:p:1-6. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.