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

Bootstrap LM tests for higher-order spatial effects in spatial linear regression models Empir Econ https://doi.org/10.1007/s00181-018-1453-4 Bootstrap LM tests for higher-order spatial effects in spatial linear regression models Zhenlin Yang Received: 15 May 2017 / Accepted: 15 January 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 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. Boot- strap 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 SARAR(p, q) model and the 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. Keywords Asymptotic pivot · Bootstrap · Heteroskedasticity · LM test · Spatial lag · Spatial error · Matrix exponential · Wild bootstrap · http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Economics Springer Journals

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

Empirical Economics , Volume OnlineFirst – May 28, 2018
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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Economics; Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Economic Theory/Quantitative Economics/Mathematical Methods
ISSN
0377-7332
eISSN
1435-8921
D.O.I.
10.1007/s00181-018-1453-4
Publisher site
See Article on Publisher Site

Abstract

Empir Econ https://doi.org/10.1007/s00181-018-1453-4 Bootstrap LM tests for higher-order spatial effects in spatial linear regression models Zhenlin Yang Received: 15 May 2017 / Accepted: 15 January 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 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. Boot- strap 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 SARAR(p, q) model and the 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. Keywords Asymptotic pivot · Bootstrap · Heteroskedasticity · LM test · Spatial lag · Spatial error · Matrix exponential · Wild bootstrap ·

Journal

Empirical EconomicsSpringer Journals

Published: May 28, 2018

References

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