Journal article
Quasi-Maximum Likelihood Estimation for a Class of Continuous-time Long-memory Processes
Journal of time series analysis, Vol.26(5), pp.691-713
First Version received April 2003
09/2005
DOI: 10.1111/j.1467-9892.2005.00422.x
Abstract
Tsai and Chan (2003) has recently introduced the Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models useful for studying long-memory data. We consider the estimation of the CARFIMA models with discrete-time data by maximizing the Whittle likelihood. We show that the quasi-maximum likelihood estimator is asymptotically normal and efficient. Finite-sample properties of the quasi-maximum likelihood estimator and those of the exact maximum likelihood estimator are compared by simulations. Simulations suggest that for finite samples, the quasi-maximum likelihood estimator of the Hurst parameter is less biased but more variable than the exact maximum likelihood estimator. We illustrate the method with a real application. © 2005 Blackwell Publishing Ltd.
Details
- Title: Subtitle
- Quasi-Maximum Likelihood Estimation for a Class of Continuous-time Long-memory Processes
- Creators
- Henghsiu Tsai - University of IowaK. S Chan - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of time series analysis, Vol.26(5), pp.691-713
- Edition
- First Version received April 2003
- DOI
- 10.1111/j.1467-9892.2005.00422.x
- ISSN
- 0143-9782
- eISSN
- 1467-9892
- Publisher
- Blackwell Publishing Ltd
- Number of pages
- 23
- Language
- English
- Date published
- 09/2005
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9984257618702771
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