IDEAS home Printed from https://ideas.repec.org/a/taf/specan/v5y2010i1p93-119.html
   My bibliography  Save this article

Bootstrap Inference in Spatial Econometrics: the J-test

Author

Listed:
  • Peter Burridge
  • Bernard Fingleton

Abstract

Abstract Kelejian (2008) introduces a J-type test for the situation in which a null linear regression model, Model0, is to be tested against one or more rival non-nested alternatives, Model1, …, Model g , where typically the competing models possess endogenous spatial lags and spatially autoregressive error processes. Concentrating on the case g=1, in this paper we examine the finite sample properties of a spatial J statistic that is asymptotically under the null, and an alternative version that is conjectured to be approximately , both introduced by Kelejian. We demonstrate numerically that the tests are excessively liberal in some leading cases and conservative in others using the relevant chi-square asymptotic approximations, and explore how far this may be corrected using a simple bootstrap resampling method. Inférence ‘bootstrap’ dans l'économétrie spatiale: le test ‘J’ Résumé Kelejian (2008) présente un test de type J pour la situation dans laquelle on doit tester un modèle a régression linéaire nulle, Model0, par rapport à une ou plusieurs alternatives concurrentes non imbriquées, Model1, …, Model g , dans laquelle les modèles concurrents possèdent généralement des retards spatiaux endogènes et des procédés d'erreur spatialement autorégressifs. En nous concentrant sur le cas g=1, nous examinons, dans la présente communication, les propriétés d'échantillon finies d'une statistique spatiale J qui se trouve asymptotiquement sous le zéro, et une version alternative supposée être égale à environ , introduites toutes les deux par Kelejian. Nous démontrons de façon numérique que les tests sont excessivement libéraux, dans certains des principaux cas, et plutôt prudents dans d'autres, en faisant usage des approximations asymptotiques au chi carré, et nous explorons la mesure dans laquelle nous pouvons le corriger en appliquant un simple processus empirique ré-échantillonné. La inferencia bootstrap en la econometría espacial: el test J Résumén Kelejian (2008) introduce un test de tipo J para la situación en que un modelo de regresión lineal nulo, Model0, se pone a prueba contra una o más alternativas rivales no anidadas, Model1, …, Model g , donde típicamente los modelos competidores poseen lapsos espaciales endógenos y procesos de error espacialmente autorregresivos. Concentrándose en el caso, g=1, este trabajo examina las propiedades de muestra finita de una estadística espacial J que es asimptóticamente bajo el nulo, y una versión alternativa que se conjetura que es aproximadamente , ambas introducidas por Kelejian. Demostramos numéricamente que los tests son excesivamente liberales en ciertos casos destacados y conservadores en otros, utilizando las aproximaciones chi cuadradas oportunas, y exploramos hasta qué punto esto podría corregirse empleando un método simple bootstrap de remuestreo.

Suggested Citation

  • Peter Burridge & Bernard Fingleton, 2010. "Bootstrap Inference in Spatial Econometrics: the J-test," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 93-119.
  • Handle: RePEc:taf:specan:v:5:y:2010:i:1:p:93-119
    DOI: 10.1080/17421770903511346
    as

    Download full text from publisher

    File URL: http://www.taylorandfrancisonline.com/doi/abs/10.1080/17421770903511346
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/17421770903511346?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Spatial econometrics; bootstrap; J-test; C; C21;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:taf:specan:v:5:y:2010:i:1:p:93-119. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.tandfonline.com/RSEA20 .

    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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RSEA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.