IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/27706.html
   My bibliography  Save this paper

Bridging logistic and OLS regression

Author

Listed:
  • Kapsalis, Constantine

Abstract

There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regression at predicting the probability of an event. OLS is still widely used in binary choice models because its coefficients are easier to interpret, while the resulting estimates tend to be close to the logit estimates anyway. Although some statistical software provide an easy way of calculating marginal effects (equivalent in interpretation to OLS coefficients) this is not always the case. This paper shows a simple way of calculating marginal effects from logistic coefficients.

Suggested Citation

  • Kapsalis, Constantine, 2010. "Bridging logistic and OLS regression," MPRA Paper 27706, University Library of Munich, Germany, revised Dec 2010.
    • Handle: RePEc:pra:mprapa:27706
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/27706/1/MPRA_paper_27706.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    2. Moffitt, Robert A., 1999. "New developments in econometric methods for labor market analysis," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 24, pages 1367-1397, Elsevier.
    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. Arturo Estrella & Anthony P. Rodrigues, 1998. "Consistent covariance matrix estimation in probit models with autocorrelated errors," Staff Reports 39, Federal Reserve Bank of New York.
    2. T.R.L. Fry & R.D. Brooks & Br. Comley & J. Zhang, 1993. "Economic Motivations for Limited Dependent and Qualitative Variable Models," The Economic Record, The Economic Society of Australia, vol. 69(2), pages 193-205, June.
    3. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    4. Maria Iacovou, 2002. "Class Size in the Early Years: Is Smaller Really Better?," Education Economics, Taylor & Francis Journals, vol. 10(3), pages 261-290.
    5. Erik Stam & Roy Thurik & Peter van der Zwan, 2010. "Entrepreneurial exit in real and imagined markets," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 19(4), pages 1109-1139, August.
    6. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    7. Y. Saks, 2016. "Socio-economic transitions on the labour market : a European benchmarking exercise," Economic Review, National Bank of Belgium, issue iii, pages 41-58, December.
    8. Kodila-Tedika, Oasis & Khalifa, Sherif, 2022. "State History and State Fragility in Sub-Saharan Africa," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 47(4), pages 39-53, December.
    9. Garcia, Philip & Hudson, Michael A. & Waller, Mark L., 1988. "The Pricing Efficiency of Agricultural Futures Markets: An Analysis of Previous Research Results," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 20(1), pages 119-130, July.
    10. Haaijer, Marinus E., 1996. "Predictions in conjoint choice experiments : the x-factor probit model," Research Report 96B22, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    11. Ashok Mishra & Barry Goodwin, 2006. "Revenue insurance purchase decisions of farmers," Applied Economics, Taylor & Francis Journals, vol. 38(2), pages 149-159.
    12. Zhang, Xuelin, 2007. "Gender Differences in Quits and Absenteeism in Canada," Analytical Studies Branch Research Paper Series 2007296e, Statistics Canada, Analytical Studies Branch.
    13. Hu, Michael Y. & Chen, Haiyang, 1996. "An empirical analysis of factors explaining foreign joint venture performance in China," Journal of Business Research, Elsevier, vol. 35(2), pages 165-173, February.
    14. Bagi, Faqir Singh, 1983. "A Logit Model Of Farmers' Decisions About Credit," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 15(2), pages 1-7, December.
    15. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    16. Monika Bütler, 2002. "The Political Feasibility of Increasing the Retirement Age: Lessons from a Ballot on the Female Retirement Age," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 9(4), pages 349-365, August.
    17. Per Botolf Maurseth, 2005. "Lovely but dangerous: The impact of patent citations on patent renewal," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(5), pages 351-374.
    18. Fiorella Kostoris Padoa Schioppa & Claudio Lupi, 2002. "Family Income and Wealth, Youth Unemployment and Active Labour Market Policies," International Review of Applied Economics, Taylor & Francis Journals, vol. 16(4), pages 407-416.
    19. Michael LaCour-Little & Michael Marschoun & Clark L. Maxam, 2002. "Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression," Journal of Real Estate Research, American Real Estate Society, vol. 24(3), pages 299-328.
    20. Alois Stutzer & Lorenz Goette & Michael Zehnder, 2011. "Active Decisions and Prosocial Behaviour: a Field Experiment on Blood Donation," Economic Journal, Royal Economic Society, vol. 121(556), pages 476-493, November.

    More about this item

    Keywords

    regression analysis;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:pra:mprapa:27706. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.