IDEAS home Printed from https://ideas.repec.org/h/spr/adschp/978-3-031-48385-1_4.html
   My bibliography  Save this book chapter

Refined GMM estimators for simultaneous equations models with network interactions

In: Advances in Applied Econometrics

Author

Listed:
  • Peter H. Egger

    (ETH Zurich)

  • Ingmar R. Prucha

    (University of Maryland)

Abstract

The paper proposes a refinement of the generalized spatial two-stage and three-stage least squares estimators for simultaneous systems of equations with network interdependence, recently introduced in Drukker (Econom Theory 1-48, 2022). Specifically, we propose a refined weighting of the moment conditions underlying those estimators. Monte Carlo simulations document that the refined weighting potentially achieves non-trivial reductions in the root mean-squared errors for the network parameters of interest.

Suggested Citation

  • Peter H. Egger & Ingmar R. Prucha, 2024. "Refined GMM estimators for simultaneous equations models with network interactions," Advanced Studies in Theoretical and Applied Econometrics, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang (ed.), Advances in Applied Econometrics, pages 71-78, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-48385-1_4
    DOI: 10.1007/978-3-031-48385-1_4
    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.

    More about this item

    Keywords

    Cliff-Ord spatial model; Two-stage least squares estimation; Three-stage least squares estimation; Generalized method of moments estimation;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

    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:spr:adschp:978-3-031-48385-1_4. 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.