Bootstrap LM tests for higher-order spatial effects in spatial linear regression models
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 statistic...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2018
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| Online Access: | https://ink.library.smu.edu.sg/soe_research/2336 https://ink.library.smu.edu.sg/context/soe_research/article/3335/viewcontent/Yang2018EE_av.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 model and the 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. |
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