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CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology KTH/CESIS Working Paper Series in Economics and Institutions of Innovation

No 258:
Median Regression for SUR Models with the Same Explanatory Varia

Ghazi Shukur () and Zangin Zeebari ()

Abstract: In this paper we introduce an interesting feature of the Generalized Least Absolute Deviations (GLAD) method for Seemingly Unrelated Regression Equations (SURE) models. Contrary to the collapse of Generalized Least Squares (GLS) parameter estimations of SURE models to the Ordinary Least Squares (OLS) estimations of the individual equations when the same regressors are common between all equations, the estimations of the proposed methodology are not identical to the Least Absolute Deviations (LAD) estimations of the individual equations. This is important since contrary to the least squares methods, one can take advantage of efficiency gain due to cross-equation correlations even if the system includes the same regressors in each equation. This kind of methodology is useful say when estimating the factors that affect firms’ innovation investments across European countries.

Keywords: Median Regression; Robustness; Efficiency; SURE Models; Innovation Investment; (follow links to similar papers)

JEL-Codes: C30; C31; (follow links to similar papers)

20 pages, October 18, 2011

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