IDEAS home Printed from https://ideas.repec.org/p/boc/fsug22/18.html
   My bibliography  Save this paper

Efficient estimation of spatial econometrics models with skewed and heavy-tailed distributed errors

Author

Listed:
  • Vincenzo Verardi

    (Université de Namur)

Abstract

In spatial econometrics, estimation of models by maximum likelihood (ML) generally relies on the assumption of normally distributed errors. While this approach leads to highly efficient estimators when the distribution is Gaussian, GMM might yield more efficient estimators if the distribution is misspecified. For the SAR model, Lee (2004) proposes an alternative QML estimator that is less sensitive to the violation of the normality assumption. In this presentation, I derive an estimator that is highly efficient for skewed and heavy-tailed distributions. More precisely, I here assume that the distribution of the errors is a Tukey g-and-h (Tgh). However, because the density function of the Tgh has no explicit form, the optimization program for the MLE needs a numeric inversion of the quantile function to fit the model, which is a computationally demanding task. To solve this difficulty, I rely on the local asymptotic normality (LAN) property of spatial econometrics models to propose an estimator that avoids such a computational burden. My Monte Carlo simulations show that our estimator outperforms the ones available as soon as the distribution of the errors departs from Gaussianity either by exhibiting heavier tails or skewness. I illustrate the usefulness of the suggested procedure relying on a trade regression.

Suggested Citation

  • Vincenzo Verardi, 2022. "Efficient estimation of spatial econometrics models with skewed and heavy-tailed distributed errors," French Stata Users' Group Meetings 2022 18, Stata Users Group.
  • Handle: RePEc:boc:fsug22:18
    as

    Download full text from publisher

    File URL: http://repec.org/frsug2022/France22_Verardi.pdf
    File Function: presentation materials
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:boc:fsug22:18. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.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.