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

Dong Wan Shin; Han Joon Kim; Won Chul Jhee

doi:10.1016/j.spl.2006.05.024

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

Dong Wan Shin, Han Joon Kim, Won Chul Jhee

Department of Statistics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	75-82
Number of pages	8
Journal	Statistics and Probability Letters
Volume	77
Issue number	1
DOIs	https://doi.org/10.1016/j.spl.2006.05.024
State	Published - 1 Jan 2007

Bibliographical note

Funding Information:
The authors are very grateful for an associate editor and a referee for many helpful comments. This work was supported by Korea Research Foundation Grant KRF-2002-042-C00008.

Keywords

Cointegration
Efficiency
Generalized least-squares estimator
Long-run covariance

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10.1016/j.spl.2006.05.024

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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.",

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AU - Shin, Dong Wan

AU - Joon Kim, Han

AU - Jhee, Won Chul

N1 - Funding Information: The authors are very grateful for an associate editor and a referee for many helpful comments. This work was supported by Korea Research Foundation Grant KRF-2002-042-C00008.

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AB - 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.

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KW - Efficiency

KW - Generalized least-squares estimator

KW - Long-run covariance

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JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

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ER -

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

Abstract

Bibliographical note

Keywords

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