LM tests of spatial dependence based on bootstrap critical values

To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which becom...

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Main Author: YANG, Zhenlin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/soe_research/1605
https://ink.library.smu.edu.sg/context/soe_research/article/2604/viewcontent/Yang_JOE3102014.pdf
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spelling sg-smu-ink.soe_research-26042020-04-02T06:22:02Z LM tests of spatial dependence based on bootstrap critical values YANG, Zhenlin To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM tests using Edgeworth expansions. Monte Carlo results show that when the conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample critical values than those based on asymptotics, and lead to significantly improved size and power. The methods are further demonstrated using more general spatial LM tests, in connection with local misspecification and unknown heteroskedasticity. 2015-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1605 info:doi/10.1016/j.jeconom.2014.10.005 https://ink.library.smu.edu.sg/context/soe_research/article/2604/viewcontent/Yang_JOE3102014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asymptotic refinements Bootstrap Edgeworth expansion LM tests Spatial dependence Size Power Local misspecification Heteroskedasticity Wild bootstrap Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Asymptotic refinements
Bootstrap
Edgeworth expansion
LM tests
Spatial dependence
Size
Power
Local misspecification
Heteroskedasticity
Wild bootstrap
Econometrics
spellingShingle Asymptotic refinements
Bootstrap
Edgeworth expansion
LM tests
Spatial dependence
Size
Power
Local misspecification
Heteroskedasticity
Wild bootstrap
Econometrics
YANG, Zhenlin
LM tests of spatial dependence based on bootstrap critical values
description To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM tests using Edgeworth expansions. Monte Carlo results show that when the conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample critical values than those based on asymptotics, and lead to significantly improved size and power. The methods are further demonstrated using more general spatial LM tests, in connection with local misspecification and unknown heteroskedasticity.
format text
author YANG, Zhenlin
author_facet YANG, Zhenlin
author_sort YANG, Zhenlin
title LM tests of spatial dependence based on bootstrap critical values
title_short LM tests of spatial dependence based on bootstrap critical values
title_full LM tests of spatial dependence based on bootstrap critical values
title_fullStr LM tests of spatial dependence based on bootstrap critical values
title_full_unstemmed LM tests of spatial dependence based on bootstrap critical values
title_sort lm tests of spatial dependence based on bootstrap critical values
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/soe_research/1605
https://ink.library.smu.edu.sg/context/soe_research/article/2604/viewcontent/Yang_JOE3102014.pdf
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