Hellinger distance estimation of nonlinear dynamical systems

Ouagnina Hili

Statistics & Probability Letters, 2003, vol. 63, issue 2, 177-184

Abstract: The present paper deals with the minimum Hellinger distance (MHD) parameter estimation of nonlinear dynamical systems with highly correlated residuals. In order to estimate the parameter of interest, we fit the residuals by an exponential autoregressive time-series model. Under some assumptions which ensure the stationarity, the existence of the moments of the stationary distribution and the strong mixing property of the fitted residuals, we establish the almost sure convergence and the asymptotic normality of the MHD estimates.

Keywords: Nonlinear; dynamical; systems; EXPAR; models; Hellinger; distance; estimation; Consistency; Asymptotic; normality (search for similar items in EconPapers)
Date: 2003
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(03)00080-4
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:63:y:2003:i:2:p:177-184

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().