[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v42y1996i7p992-1003.html
   My bibliography  Save this article

Optimizing Multinomial Logit Profit Functions

Author

Listed:
  • Ward Hanson

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Kipp Martin

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract
The multinomial logit model is a standard approach for determining the probability of purchase in product line problems. When the purchase probabilities are multiplied by product contribution margins, the resulting profit function is generally nonconcave. Because of this, standard nonlinear search procedures may terminate at a local optimum which is far from the global optimum. We present a simple procedure designed to alleviate this problem. The key idea of this procedure is to find a "path" of prices from the global optimum of a related, but concave logit profit function, to the global optimum of the true (but nonconcave) logit profit function.

Suggested Citation

  • Ward Hanson & Kipp Martin, 1996. "Optimizing Multinomial Logit Profit Functions," Management Science, INFORMS, vol. 42(7), pages 992-1003, July.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:7:p:992-1003
    DOI: 10.1287/mnsc.42.7.992
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.42.7.992
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.42.7.992?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
    ---><---

    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:inm:ormnsc:v:42:y:1996:i:7:p:992-1003. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.