Minimum distance estimators for random coefficient autoregressive models

Lianfen Qian

Statistics & Probability Letters, 1996, vol. 29, issue 3, 251-262

Abstract: This paper discusses a class of minimum distance (MD) estimators for a class of pth-order random coefficient autoregressive (RCAR(p)) models. These estimators are defined via certain weighted empiricals as in Koul (1986). The class of estimators considered includes the least absolute deviation estimator and an analogue of the Hodges-Lehmann estimator. The paper contains a proof of the asymptotic normality of these estimators and a simulation study. It is observed that the RCAR(2) model with the Hodges-Lehmann type estimator fits the Canadian lynx data at least as well as with the least square estimator.

Keywords: Asymptotic; uniform; quadraticity; Asymptotic; normality; Hodges-Lehmann; type; estimator (search for similar items in EconPapers)
Date: 1996
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(95)00180-8
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:29:y:1996:i:3:p:251-262

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 ().