IDEAS home Printed from https://ideas.repec.org/h/spr/adspcp/978-3-319-30196-9_7.html
   My bibliography  Save this book chapter

A Bayesian Spatial Interaction Model Variant of the Poisson Pseudo-Maximum Likelihood Estimator

In: Spatial Econometric Interaction Modelling

Author

Listed:
  • James P. LeSage

    (Texas State University)

  • Esra Satici

    (General Directorate of Turkish Highways)

Abstract

There are several econometric advantages to the Poisson pseudo-maximum likelihood (PPML) approach to estimating relationships involving flows (Santos Silva and Tenreyro 2010). One is that the coefficients on logged explanatory variables (X) in the (exponential) relationship involving non-logged flow magnitudes as the dependent variable (y) can be interpreted as the elasticity of the conditional expectation of y i with respect to X i .

Suggested Citation

  • James P. LeSage & Esra Satici, 2016. "A Bayesian Spatial Interaction Model Variant of the Poisson Pseudo-Maximum Likelihood Estimator," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 121-143, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-30196-9_7
    DOI: 10.1007/978-3-319-30196-9_7
    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:adspcp:978-3-319-30196-9_7. 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.