IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v8y1980i3p309-340.html
   My bibliography  Save this article

Collinearity, Ridge Regression, and Investigator Judgment

Author

Listed:
  • James Fennessey

    (Johns Hopkins University)

  • Ronald D'Amico

    (Johns Hopkins University)

Abstract

Several recent articles have suggested that ridge regression may provide an "optimal" procedure for dealing with the problems created by highly collinear regressors in linear models. In this article, we review the consequences of collinearity among the regressors in a well-specified structural equation model and the several variants of ridge regression that may be considered as possible responses to such collinearity. Based on thts review and on application of several ridge regression methods to an actual structural equation model in which collinearity is high, it becomes clear that one or another form of investigator judgment is unavoidable when any specific estimates are obtained via a ridge adjustment. The article indicates how the several ridge techniques implicitly involve distinct criteria whereby an estimator should be judged and some of the complications involved in each.

Suggested Citation

  • James Fennessey & Ronald D'Amico, 1980. "Collinearity, Ridge Regression, and Investigator Judgment," Sociological Methods & Research, , vol. 8(3), pages 309-340, February.
  • Handle: RePEc:sae:somere:v:8:y:1980:i:3:p:309-340
    DOI: 10.1177/004912418000800304
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/004912418000800304
    Download Restriction: no

    File URL: https://libkey.io/10.1177/004912418000800304?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-131, February.
    2. Haitovsky, Yoel, 1969. "Multicollinearity in Regression Analysis: Comment," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 486-489, November.
    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. A. D. Owen, 1979. "The Demand for Wine in Australia, 1955–1977," The Economic Record, The Economic Society of Australia, vol. 55(3), pages 230-235, September.
    2. Sürücü, Lütfi & YIKILMAZ, İbrahim & MASLAKÇI, Ahmet, 2022. "Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations," OSF Preprints fgd4e, Center for Open Science.
    3. Wan Fung Lee & Jeffrey W. Bulcock & Wo Shun Luk, 1984. "The R2 Ridge Trace in 2SLS Regression Estimation," Sociological Methods & Research, , vol. 13(2), pages 219-249, November.
    4. Jeffrey W. Bulcock & Wan Fung Lee, 1983. "Normalization Ridge Regression in Practice," Sociological Methods & Research, , vol. 11(3), pages 259-303, February.
    5. Cui, Qiang & Kuang, Hai-bo & Wu, Chun-you & Li, Ye, 2014. "The changing trend and influencing factors of energy efficiency: The case of nine countries," Energy, Elsevier, vol. 64(C), pages 1026-1034.
    6. Tambi, N. E. & Mukhebi, W. A. & Maina, W. O. & Solomon, H. -M., 1999. "Probit analysis of livestock producers' demand for private veterinary services in the high potential agricultural areas of Kenya," Agricultural Systems, Elsevier, vol. 59(2), pages 163-176, February.
    7. Suman Prosad Saha, 2017. "The Influence Of Manufacturer Attitude, Brand Strength And Profits On Distributors’ Overall Satisfaction: Evidence From Bangladesh," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 10(1), pages 45-56.
    8. Habib-ur Rahman & Muhammad Waqas Yousaf & Nageena Tabassum, 2020. "Bank-Specific and Macroeconomic Determinants of Profitability: A Revisit of Pakistani Banking Sector under Dynamic Panel Data Approach," IJFS, MDPI, vol. 8(3), pages 1-19, July.
    9. Ullah, A. & Vinod, H. D. & Kadiyala, R. K., 1978. "A Family Of Improved Ordinary Ridge Estimators," Econometric Institute Archives 272169, Erasmus University Rotterdam.
    10. Gary Smith, 1974. "Multicollinearity and Forecasting," Cowles Foundation Discussion Papers 383, Cowles Foundation for Research in Economics, Yale University.
    11. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    12. Niranjan Tripathy & Richard L. Peterson, 1991. "The Relationship Between Otc Bid-Ask Spreads And Dealer Size: The Impact Of Order-Processing And Diversification Costs," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(2), pages 117-127, June.
    13. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    14. Thomas M Fullerton Jr & Eiichi Araki, 2004. "A Theoretical Model of Industrial Economy Inflationary Dynamics," Macroeconomics 0408007, University Library of Munich, Germany.
    15. V. Srivastava & A. Chaturvedi, 1983. "Some properties of the distribution of an operational ridge estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 30(1), pages 227-237, December.
    16. Alkhamisi, M.A. & Shukur, Ghazi, 2007. "Developing Ridge Parameters for SUR Models," Working Paper Series in Economics and Institutions of Innovation 80, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    17. Zeebari, Zangin & Shukur, Ghazi & Kibria, B. M. Golam, 2010. "Modified Ridge Parameters for Seemingly Unrelated Regression Model," HUI Working Papers 43, HUI Research.
    18. Vinod, H. D., 1995. "Double bootstrap for shrinkage estimators," Journal of Econometrics, Elsevier, vol. 68(2), pages 287-302, August.
    19. David A. Belsley, 1976. "Multicollinearity: Diagnosing its Presence and Assessing the Potential Damage It Causes Least Squares Estimation," NBER Working Papers 0154, National Bureau of Economic Research, Inc.
    20. Ishmael Ackah, 2014. "Determinants of natural gas demand in Ghana," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 38(3), pages 272-295, September.

    More about this item

    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:sae:somere:v:8:y:1980:i:3:p:309-340. 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: SAGE Publications (email available below). General contact details of provider: .

    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.