IDEAS home Printed from https://ideas.repec.org/p/zbw/faucse/332000.html
   My bibliography  Save this paper

Sequential regression: a neodescriptive approach to multicollinearity

Author

Listed:
  • Fickel, Norman

Abstract

Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of the regressand is only valid if the regressors are weakly correlated, and they are useless when the degree of multicollinearity is high. Despite these obvious flaws there is a lack of alternative ideas to measure influences. On that score this paper proposes two new coefficients of influence: (1) A supplementary coefficient measures the additional influence of a regressor when certain variables are already taken into account. (2) A particular coefficient, which is a mean of certain supplementary coefficients, allocates the influence of a regressor within the collective influence of all regressors. Both new coefficients can directly be interpreted as average changes of the regressand

Suggested Citation

  • Fickel, Norman, 2000. "Sequential regression: a neodescriptive approach to multicollinearity," Discussion Papers 33/2000, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    • Handle: RePEc:zbw:faucse:332000
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/29605/1/61312314X.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. G. Newton & D. J. Spurrell, 1967. "A Development of Multiple Regression for the Analysis of Routine Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 51-64, March.
    2. Douglas M. Hawkins, 1973. "On the Investigation of Alternative Regressions by Principal Component Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(3), pages 275-286, 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. Norman Fickel, 2001. "Sequential Regression: A Neodescriptive Approach to Multicollinearity," EERI Research Paper Series EERI_RP_2001_09, Economics and Econometrics Research Institute (EERI), Brussels.
    2. Norman Fickel, 2000. "Sequential Regression: A Neodescriptive Approach to Multicollinearity," Econometrics 0004009, University Library of Munich, Germany.
    3. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    4. 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.
    5. Gene Golub & Virginia Klema & G. W. Stewart, 1977. "Rosetak Document 4: Rank Degeneracies and Least Square Problems," NBER Working Papers 0165, National Bureau of Economic Research, Inc.
    6. Enache, Daniel & Weihs, Claus, 2004. "Importance Assessment of Correlated Predictors in Business Cycles Classification," Technical Reports 2004,66, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    7. Francesco Tosello & Andrea Guala & Dario Leone & Carlo Camporeale & Giulia Bruno & Luca Ridolfi & Franco Veglio & Alberto Milan, 2016. "Central Pressure Appraisal: Clinical Validation of a Subject-Specific Mathematical Model," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-10, March.
    8. Bauer, Jan O. & Drabant, Bernhard, 2023. "Regression based thresholds in principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 193(C).

    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:zbw:faucse:332000. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vierlde.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.