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

• 主要作者: YANG, Zhenlin
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2018
• 主題: Asymptotic pivot; Bootstrap; Heteroskedasticity; LM test; Spatial lag; Spatial error; Matrix exponential; Wild bootstrap; Bootstrap critical values; Econometrics
• 在線閱讀: https://ink.library.smu.edu.sg/soe_research/2336; https://ink.library.smu.edu.sg/context/soe_research/article/3335/viewcontent/Yang2018EE_av.pdf

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.