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Bootstrap LM tests for higher-order spatial effects in spatial linear regression models

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  • Zhenlin Yang

    (Singapore Management University)

Abstract

This paper first extends the methodology of Yang (J Econom 185:33–59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the $$\mathtt{SARAR}(p,q)$$ SARAR ( p , q ) model and the $$\mathtt{MESS}(p,q)$$ MESS ( p , q ) model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order spatial effects, and a complete set of LM and bootstrap LM tests robust against both unknown heteroskedasticity and non-normality. Monte Carlo experiments are run, and results show an excellent performance of the bootstrap LM-type tests.

Suggested Citation

  • Zhenlin Yang, 2018. "Bootstrap LM tests for higher-order spatial effects in spatial linear regression models," Empirical Economics, Springer, vol. 55(1), pages 35-68, August.
    • Handle: RePEc:spr:empeco:v:55:y:2018:i:1:d:10.1007_s00181-018-1453-4
    DOI: 10.1007/s00181-018-1453-4
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    References listed on IDEAS

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    Cited by:

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    3. Zhenlin Yang, 2021. "Joint tests for dynamic and spatial effects in short panels with fixed effects and heteroskedasticity," Empirical Economics, Springer, vol. 60(1), pages 51-92, January.

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