IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01868541.html
   My bibliography  Save this paper

Panel data models with spatially dependent nested random effects

Author

Listed:
  • Bernard Fingleton

    (Department of Land Economy - University of Cambridge)

  • Julie Le Gallo

    (CESAER - Centre d'Economie et de Sociologie Rurales Appliquées à l'Agriculture et aux Espaces Ruraux - INRA - Institut National de la Recherche Agronomique - AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement)

  • Alain Pirotte

    (CRED - Cognitive Research and Enactive Design - COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne)

Abstract

This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane-Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties.

Suggested Citation

  • Bernard Fingleton & Julie Le Gallo & Alain Pirotte, 2018. "Panel data models with spatially dependent nested random effects," Post-Print hal-01868541, HAL.
    • Handle: RePEc:hal:journl:hal-01868541
    DOI: 10.1111/jors.12327
    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.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ye, Qianting & Liang, Huajie & Lin, Kuan-Pin & Long, Zhihe, 2019. "Hierarchically spatial autoregressive and moving average error model," Economic Modelling, Elsevier, vol. 76(C), pages 14-30.
    2. Linus Holtermann & Christian Hundt, 2018. "Hierarchically structured determinants and phase related patterns of economic resilience. An empirical case study for European regions," Working Papers on Innovation and Space 2018-02, Philipps University Marburg, Department of Geography.

    More about this item

    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:hal:journl:hal-01868541. 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.