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Median Regression for SUR Models with the Same Explanatory Varia

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  • Shukur, Ghazi

    () (Jönköping International Business School)

  • Zeebari, Zangin

    () (Jönköping International Business School)

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.

Suggested Citation

  • Shukur, Ghazi & Zeebari, Zangin, 2011. "Median Regression for SUR Models with the Same Explanatory Varia," Working Paper Series in Economics and Institutions of Innovation 258, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
  • Handle: RePEc:hhs:cesisp:0258
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    File URL: https://static.sys.kth.se/itm/wp/cesis/cesiswp258.pdf
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    References listed on IDEAS

    as
    1. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
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    Keywords

    Median Regression; Robustness; Efficiency; SURE Models; Innovation Investment;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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