Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors
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Title
- Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors
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Author(s)
- Shin, Dong Wan; Kim, Han Joon; Jhee, Won-Chul
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Alternative Author(s)
- 김한준
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Publication Year
- 2007-01-01
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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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ISSN
- 0167-7152
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/4741
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DOI
- 10.1016/j.spl.2006.05.024
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Bibliographic Citation
- STATISTICS & PROBABILITY LETTERS, v.77, no.1, pp.75 - 82, 2007
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Publisher
- ELSEVIER SCIENCE BV
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Subject
- SEEMINGLY UNRELATED REGRESSIONS; VARIABLES; GLS; OLS
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Keywords
- cointegration; efficiency; generalized least-squares estimator; long-run covariance
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Type
- Article
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Language
- English
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Document Type
- Article
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