IDEAS home Printed from https://ideas.repec.org/p/siu/wpaper/10-2015.html
   My bibliography  Save this paper

Specification Test for Spatial Autoregressive Models

Author

Listed:
  • Su Liangjun

    (Singapore Management University)

  • Xi Qu

    (Shanghai Jiao Tong University)

Abstract

This paper considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove the consistency of our test. To improve the finite sample performance of our test, we also propose a residual-based wild bootstrap and justify its asymptotic validity. We conduct a small set of Monte Carlo simulations to investigate the finite sample properties of our tests. Finally, we apply the test to two empirical datasets: the vote cast and the economic growth rate. We reject the linear spatial autoregressive model in the vote cast example but fail to reject it in the economic growth rate example.

Suggested Citation

  • Su Liangjun & Xi Qu, 2015. "Specification Test for Spatial Autoregressive Models," Working Papers 10-2015, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:10-2015
    as

    Download full text from publisher

    File URL: http://ink.library.smu.edu.sg/soe_research/1742/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Generalized method of moments; Nonlinearity; Spatial autoregression; Spatial dependence; Specification test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:siu:wpaper:10-2015. 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: QL THor (email available below). General contact details of provider: https://edirc.repec.org/data/sesmusg.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.