IDEAS home Printed from https://ideas.repec.org/p/hhs/cesisp/0258.html
   My bibliography  Save this paper

Median Regression for SUR Models with the Same Explanatory Varia

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://static.sys.kth.se/itm/wp/cesis/cesiswp258.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    2. 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.
    3. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    4. Zeebari, Zangin & Shukur, Ghazi, 2009. "Developing Median Regression for SURE Models - with Application to 3-Generation Immigrants’ data in Sweden," Working Paper Series in Economics and Institutions of Innovation 183, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zangin Zeebari & Ghazi Shukur, 2023. "On The Least Absolute Deviations Method for Ridge Estimation of Sure Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(14), pages 4773-4791, July.
    2. Shukur, Ghazi & Zeebari, Zangin, 2011. "On the median regression for SURE models with applications to 3-generation immigrants data in Sweden," Economic Modelling, Elsevier, vol. 28(6), pages 2566-2578.
    3. Ghazi Shukur & Zangin Zeebari, 2012. "Median regression for SUR models with the same explanatory variables in each equation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1765-1779, April.
    4. Frank R. Lichtenberg, 2014. "Has Medical Innovation Reduced Cancer Mortality?," CESifo Economic Studies, CESifo Group, vol. 60(1), pages 135-177.
    5. García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
    6. Cowling, Marc & Ughetto, Elisa & Lee, Neil, 2018. "The innovation debt penalty: Cost of debt, loan default, and the effects of a public loan guarantee on high-tech firms," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 166-176.
    7. Alhassan Abdul-Wakeel Karakara & Evans Osabuohien, 2020. "ICT adoption, competition and innovation of informal firms in West Africa: a comparative study of Ghana and Nigeria," Journal of Enterprising Communities: People and Places in the Global Economy, Emerald Group Publishing Limited, vol. 14(3), pages 397-414, June.
    8. ManYing Kang & Marcel Ausloos, 2017. "An Inverse Problem Study: Credit Risk Ratings as a Determinant of Corporate Governance and Capital Structure in Emerging Markets: Evidence from Chinese Listed Companies," Economies, MDPI, vol. 5(4), pages 1-23, November.
    9. Vitaliy Roud & Thomas Wolfgang Thurner, 2018. "The Influence of State‐Ownership on Eco‐Innovations in Russian Manufacturing Firms," Journal of Industrial Ecology, Yale University, vol. 22(5), pages 1213-1227, October.
    10. Laura Barbieri & Daniela Bragoli & Flavia Cortelezzi & Giovanni Marseguerra, 2015. "Public Support to Innovation Strategies," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1509, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    11. Massimo Colombo & Annalisa Croce & Samuele Murtinu, 2014. "Ownership structure, horizontal agency costs and the performance of high-tech entrepreneurial firms," Small Business Economics, Springer, vol. 42(2), pages 265-282, February.
    12. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    13. Olusola O. Ololade & Palesa P. Rametse, 2018. "Determining factors that enable managers to implement an environmental management system for sustainable construction: A case study in Johannesburg," Business Strategy and the Environment, Wiley Blackwell, vol. 27(8), pages 1720-1732, December.
    14. Leonard F.S. Wang & Arijit Mukherjee, 2014. "Patent Protection, Innovation and Technology Licensing," Australian Economic Papers, Wiley Blackwell, vol. 53(3-4), pages 245-254, December.
    15. Schankerman, Mark & Schuett, Florian, 2016. "Screening for Patent Quality," CEPR Discussion Papers 11688, C.E.P.R. Discussion Papers.
    16. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    17. Christoph P. Kiefer & Pablo Del Río González & Javier Carrillo‐Hermosilla, 2019. "Drivers and barriers of eco‐innovation types for sustainable transitions: A quantitative perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 155-172, January.
    18. Richard Harris & John Moffat, 2011. "R&D, Innovation and Exporting," SERC Discussion Papers 0073, Centre for Economic Performance, LSE.
    19. Chen, Qihao & Huang, Zhuo & Liang, Fang, 2023. "Measuring systemic risk with high-frequency data: A realized GARCH approach," Finance Research Letters, Elsevier, vol. 54(C).
    20. Wipo, 2011. "World Intellectual Property Report 2011- The Changing Face of Innovation," WIPO Economics & Statistics Series, World Intellectual Property Organization - Economics and Statistics Division, number 2011:944, April.

    More about this item

    Keywords

    Median Regression; Robustness; Efficiency; SURE Models; Innovation Investment;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:cesisp:0258. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vardan Hovsepyan (email available below). General contact details of provider: https://edirc.repec.org/data/cekthse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.