Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors SCIE SCOPUS

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Title
Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors
Author(s)
Shin, Dong Wan; Kim, Han Joon; Jhee, Won-Chul
Publication Year
2007-01-01
Abstract
For seemingly unrelated regression (SUR) models with integrated regressors, two sufficient conditions are identified, under which the ordinary least-squares estimator (OLSE) is asymptotically efficient. The first condition is that every pair of regressor processes are cointegrated in a specific way that one regressor is a linear combination of the other regressor up to a zero-mean stationary error and the second condition is that, for every pair of regressor processes, the pair of error processes deriving the regressor processes have zero long-run covariance. (c) 2006 Elsevier B.V: All rights reserved.
ISSN
0167-7152
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/4741
DOI
10.1016/j.spl.2006.05.024
Bibliographic Citation
STATISTICS & PROBABILITY LETTERS, v.77, no.1, pp.75 - 82, 2007
Publisher
ELSEVIER SCIENCE BV
Subject
SEEMINGLY UNRELATED REGRESSIONS; VARIABLES; GLS; OLS
Keywords
cointegration; efficiency; generalized least-squares estimator; long-run covariance
Type
Article
Language
English
Document Type
Article
Publisher
ELSEVIER SCIENCE BV
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