Asymptotic efficiency of the OLS estimator with singular limiting sample moment matrices

Yoshimasa Uematsu

Statistics & Probability Letters, 2016, vol. 114, issue C, 104-110

Abstract: In the literature on time series analysis, Grenander and Rosenblatt’s theorem is necessary to judge the efficiency of OLS estimators with the requirement of Grenander’s conditions. However, without the conditions, it is not obvious whether the estimator is efficient. In this study, a model with an asymptotically efficient OLS estimator is presented, regardless whether one of the conditions is not satisfied. The regression model is specified with polynomial regressors of a slowly varying function and general stationary disturbances. The regressors are known to display asymptotic singularity in the sample moment matrices, and thereby, Grenander’s condition fails.

Keywords: Stationary time series; OLS estimator; Grenander–Rosenblatt theorem; Asymptotic relative efficiency; Asymptotically collinear regressor; Slowly varying function (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715216300177
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:114:y:2016:i:c:p:104-110

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

DOI: 10.1016/j.spl.2016.03.018

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 ().