IDEAS home Printed from https://ideas.repec.org/h/spr/conchp/978-981-99-4902-1_3.html
   My bibliography  Save this book chapter

Random Logit Model: An Application to US Soft Drink Differentiated Demand Estimation

In: Applied Econometric Analysis Using Cross Section and Panel Data

Author

Listed:
  • Benaissa Chidmi

    (Department of Agricultural and Applied Economics, Texas Tech University)

Abstract

This chapter attempts to provide an overview of estimating demand for differentiated products using the random coefficient multinomial logit model, also known as the BLP model. The chapter discusses reasons for the BLP’s superiority over traditional demand models, such as the AIDS models. It also describes recent development in the BLP-type literature, especially dealing with computational issues. In addition, the chapter covers the empirical process to estimate the iBLP model, starting with the numerical integration methods, simulation of observed and unobserved consumers’ characteristics, contraction mapping, endogeneity, and the generalized method of moments (GMM) estimation, and the existing software and packages to estimate the random coefficient logit model. Finally, the chapter offers an empirical illustration for estimating differentiated demand for soft drinks.

Suggested Citation

  • Benaissa Chidmi, 2023. "Random Logit Model: An Application to US Soft Drink Differentiated Demand Estimation," Contributions to Economics, in: Deep Mukherjee (ed.), Applied Econometric Analysis Using Cross Section and Panel Data, chapter 0, pages 61-91, Springer.
    • Handle: RePEc:spr:conchp:978-981-99-4902-1_3
    DOI: 10.1007/978-981-99-4902-1_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:conchp:978-981-99-4902-1_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.