 An adaptive estimator of the memory parameter and the goodness-of-fit test using a multidimensional increment ratio statisticJean-Marc Bardet and Béchir Dola Journal of Multivariate Analysis, 2012, vol. 105, issue 1, 222-240 Abstract: The increment ratio (IR) statistic was first defined and studied in Surgailis et al. (2007) [19] for estimating the memory parameter either of a stationary or an increment stationary Gaussian process. Here three extensions are proposed in the case of stationary processes. First, a multidimensional central limit theorem is established for a vector composed by several IR statistics. Second, a goodness-of-fit χ2-type test can be deduced from this theorem. Finally, this theorem allows to construct adaptive versions of the estimator and the test which are studied in a general semiparametric frame. The adaptive estimator of the long-memory parameter is proved to follow an oracle property. Simulations attest to the interesting accuracies and robustness of the estimator and the test, even in the non Gaussian case. Keywords: Long-memory Gaussian processes; Goodness-of-fit test; Estimation of the memory parameter; Minimax adaptive estimator (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:105:y:2012:i:1:p:222-240 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2011.09.003 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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