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Parameter estimation for INAR processes based on high-order statistics

Title
Parameter estimation for INAR processes based on high-order statistics
Type
Chapter or Part of a Book
Year
2008
Authors
Isabel Silva
(Author)
FEUP
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M. Eduarda Silva
(Author)
FEP
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Book
Pages: 965-972
ISBN: 9783790820836
Electronic ISBN: 9783790820843
Scientific classification
FOS: Natural sciences > Mathematics
CORDIS: Physical sciences > Mathematics > Statistics
Other information
Abstract (EN): The high-order statistics (moments and cumulants of order higher than two) have been widely applied in several fields, specially in problems where it is conjectured a lack of Gaussianity and/or non-linearity. Since the INteger-valued AutoRegressive, INAR, models are non-Gaussian, the high-order statistics can provide additional information that allows a better characterization of these processes. Thus, an estimation method for the parameters of an INAR model, based on Least Squares applied on third-order moments is proposed. The results of a Monte Carlo study, to investigate the performance of the estimator, are presented and the method is applied to a set of real data.
Language: English
Type (Professor's evaluation): Scientific
Contact: ims@fe.up.pt
No. of pages: 8
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