Journal article
Maximum likelihood estimation of linear continuous time long memory processes with discrete time data
Journal of the Royal Statistical Society. Series B, Statistical methodology, Vol.67(5), pp.703-716
2005
DOI: 10.1111/j.1467-9868.2005.00522.x
Abstract
We develop a new class of time continuous autoregressive fractionally integrated moving average (CARFIMA) models which are useful for modelling regularly spaced and irregularly spaced discrete time long memory data. We derive the autocovariance function of a stationary CARFIMA model and study maximum likelihood estimation of a regression model with CARFIMA errors, based on discrete time data and via the innovations algorithm. It is shown that the maximum likelihood estimator is asymptotically normal, and its finite sample properties are studied through simulation. The efficacy of the approach proposed is demonstrated with a data set from an environmental study. © 2005 Royal Statistical Society.
Details
- Title: Subtitle
- Maximum likelihood estimation of linear continuous time long memory processes with discrete time data
- Creators
- Henghsiu Tsai - Academia SinicaK. S Chan - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Royal Statistical Society. Series B, Statistical methodology, Vol.67(5), pp.703-716
- Publisher
- Blackwell
- DOI
- 10.1111/j.1467-9868.2005.00522.x
- ISSN
- 1369-7412
- eISSN
- 1467-9868
- Grant note
- DOI: 10.13039/501100001868, name: National Science Council, award: NSC 91-2118-M-001-011; DOI: 10.13039/100000001, name: National Science Foundation, award: DMS-0405267
- Language
- English
- Date published
- 2005
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9984257616602771
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