Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2018

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 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.

Keywords

Asymptotic pivot, Bootstrap, Heteroskedasticity, LM test, Spatial lag, Spatial error, Matrix exponential, Wild bootstrap, Bootstrap critical values

Discipline

Econometrics

Research Areas

Econometrics

Publication

Empirical Economics

Volume

55

Issue

1

First Page

35

Last Page

68

ISSN

0377-7332

Identifier

10.1007/s00181-018-1453-4

Publisher

Springer (part of Springer Nature): Springer Open Choice Hybrid Journals

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s00181-018-1453-4

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Econometrics Commons

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