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

Authors
Shin, Dong WanKim, Han JoonJhee, Won-Chul
Issue Date
1-Jan-2007
Publisher
ELSEVIER SCIENCE BV
Keywords
cointegration; efficiency; generalized least-squares estimator; long-run covariance
Citation
STATISTICS & PROBABILITY LETTERS, v.77, no.1, pp.75 - 82
Journal Title
STATISTICS & PROBABILITY LETTERS
Volume
77
Number
1
Start Page
75
End Page
82
URI
https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/23648
DOI
10.1016/j.spl.2006.05.024
ISSN
0167-7152
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.
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