On uniform asymptotic normality of sequential least squares estimators for the parameters in a stable AR(p)

L. Galtchouk and V. Konev

Journal of Multivariate Analysis, 2004, vol. 91, issue 2, 119-142

Abstract: For a stable autoregressive process of order p with unknown vector parameter [theta], it is shown that under a sequential sampling scheme with the stopping time defined by the trace of the observed Fisher information matrix, the least-squares estimator of [theta] is asymptotically normally distributed uniformly in [theta] belonging to any compact set in the parameter region.

Keywords: Autoregressive; process; Least-squares; estimator; Sequential; estimation; Uniform; asymptotic; normality (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047-259X(03)00081-2
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:jmvana:v:91:y:2004:i:2:p:119-142

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

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().