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
Citation
YANG, Zhenlin.
Bootstrap LM tests for higher-order spatial effects in spatial linear regression models. (2018). Empirical Economics. 55, (1), 35-68.
Available at: https://ink.library.smu.edu.sg/soe_research/2336
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00181-018-1453-4