IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-04986047.html
   My bibliography  Save this paper

Efficient parametric estimation of the spatial autoregressive model

Author

Listed:
  • Nicolas Debarsy

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Vincenzo Verardi

    (Louvain Research Institute in Management and Organizations (LouRIM))

  • Catherine Vermandele

    (LMTD - Laboratoire de méthodologie du traitement des données - ULB - Université libre de Bruxelles)

Abstract

This paper introduces a new one-step parametric estimation method for spatial autoregressive (SAR) models, providing an efficient estimator for any error distribution with a defined quantile function. Based on Le Cam's Local Asymptotic Normality (LAN) theory, it extends the maximum likelihood approach to cases like the Laplace distribution, which lacks a globally defined first derivative.\We further develop this estimator for two highly flexible distributions: Tukey's gand-h and Pewsey and Jones's sinh-arcsinh (SAS), designed to capture skewness and non-normal tail weight. These flexible distributions mitigate the risks of distributional misspecification by approximating a wide range of parametric distributions. Monte Carlo simulations assess finite-sample performance, showing that our estimator outperforms traditional parametric spatial methods when the error distribution deviates from normality and is well-approximated by these flexible alternatives.

Suggested Citation

  • Nicolas Debarsy & Vincenzo Verardi & Catherine Vermandele, 2025. "Efficient parametric estimation of the spatial autoregressive model," Working Papers hal-04986047, HAL.
    • Handle: RePEc:hal:wpaper:hal-04986047
    Note: View the original document on HAL open archive server: https://hal.science/hal-04986047v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04986047v1/document
    Download Restriction: no
    ---><---

    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:hal:wpaper:hal-04986047. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.