Limit distributions for linear programming time series estimators

Paul D. Feigin and Sidney I. Resnick

Stochastic Processes and their Applications, 1994, vol. 51, issue 1, 135-165

Abstract: We consider stationary autoregressive processes of order p which have positive innovations. We propose consistent parameter estimators based on linear programming. Under conditions, including regular variation of either the left or right tail of the innovations distribution, we prove that the estimators have a limit distribution. The rate of convergence of our estimator is favorable compared with the Yule--Walker estimator under comparable circumstances.

Keywords: Poisson; processes; Linear; programming; Autoregressive; processes; Parameter; estimation; Weak; convergence; Consistency; Time; series; analysis (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(94)90022-1
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:spapps:v:51:y:1994:i:1:p:135-165

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

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().