High-correlated residuals improved estimation in the high-dimensional SUR model |
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Authors: | Li Zhao Liang Yan |
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Institution: | School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China |
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Abstract: | This article focuses on estimating regression coefficients in the high-dimensional seemingly unrelated regression model. When the number of equations exceeds that of the observations, both the maximum likelihood estimator and Zellner’s two-stage estimator do not exist. As an alternative, we propose a two-stage conditional expectation improved estimator. The new estimator is further improved by the high-correlated residuals, and the high correlation is determined by hypothesis testings. Simulations show that the new estimator outperforms the ordinary least-squares estimator in terms of mean square errors, especially when high-correlated residuals exist between the equations. |
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Keywords: | High dimension Regression coefficients Seemingly unrelated regression model Significant correlation Two-stage improved estimator |
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