Wavelet-Based Estimation Procedures For Seasonal Long-Memory Models
The appearance of long-range dependence has been observed in a wide variety of real-word time series. So called long-memory models, which exhibit a slowly decaying autocovariance sequence and a pole at frequency zero in their spectral density function, have been used to characterize long-range dependence parsimoniously. A generalization of such models allows the pole in the spectral density function to be placed anywhere in the frequency interval causing a slowly decaying oscillating autocovariance sequence. This is known as the so called seasonal long-memory model. While an exact method for maximizing the likelihood exists and a semiparametric Whittle approximation has been proposed, we investigate two estimating procedures using the discrete wavelet packet transform: an approximate maximum likelihood method and an ordinary least squares method. We utilize the known decorrelating properties of the wavelet transform to allow us to assume a simplified variance-covariance structure for the seasonal long-memory model. We describe our computational procedures and explore the versatility gained by using the wavelet transform. As an example, we fit a seasonal long-memory model to an observed time series. The proposed wavelet-based techniques offer useful and computationally efficient alternatives to previous time and frequency domain methods.
|Date of creation:||05 Jul 2000|
|Date of revision:|
|Contact details of provider:|| Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain|
Fax: +34 93 542 17 46
Web page: http://enginy.upf.es/SCE/
More information through EDIRC
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.:
- Josu Arteche & Peter M. Robinson, 1998. "Semiparametric inference in seasonal and cyclical long memory processes," LSE Research Online Documents on Economics 2203, London School of Economics and Political Science, LSE Library.
- Mark J. Jensen, 1998.
"An Approximate Wavelet MLE of Short and Long Memory Parameters,"
9802003, EconWPA, revised 21 Jun 1999.
- Jensen Mark J., 1999. "An Approximate Wavelet MLE of Short- and Long-Memory Parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(4), pages 1-17, January.
- Mark J. Jensen, 1999. "An Approximate Wavelet MLE of Short- and Long-Memory Parameters," Computing in Economics and Finance 1999 1243, Society for Computational Economics.
- Ignacio N. Lobato, 1997. "Semiparametric estimation of seasonal long memory models: theory and an application to the modeling of exchange rates," Investigaciones Economicas, Fundación SEPI, vol. 21(2), pages 273-296, May.
- Ooms, M., 1995. "Flexible Seasonal Long Memory and Economic Time Series," Econometric Institute Research Papers EI 9515-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Josu Arteche & Peter M. Robinson, 1998. "Seasonal and cyclical long memory," LSE Research Online Documents on Economics 2241, London School of Economics and Political Science, LSE Library.
- Mark J. Jensen, 1997.
"Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter,"
- Jensen, Mark J, 1999. "Using wavelets to obtain a consistent ordinary least squares estimator of the long-memory parameter," MPRA Paper 39152, University Library of Munich, Germany.
When requesting a correction, please mention this item's handle: RePEc:sce:scecf0:148. 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: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.