Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests

Julie Le Gallo, Fernando A. López () and Coro Chasco Yrigoyen
Additional contact information
Fernando A. López: Technical University of Cartagena

The Annals of Regional Science, 2020, vol. 64, issue 2, No 4, 287-312

Abstract: Abstract The aim of this paper is to develop a spatial group-wise heteroskedasticity test based on the scan approach, specifically developed for spatial autocorrelation regression models (spatial lag and spatial error models): the “scan-LM test.” Based on the Lagrange multiplier (LM) principle, its main advantage lies in its comparative ease of implementation as it is not necessary to obtain the maximum likelihood estimations for the alternative hypothesis. Moreover, when rejecting the null hypothesis, this test identifies the shape and size of the spatial clusters with different residual variance, a feature which proves very useful for specification search of the regression model. Another important benefit of the scan-LM test is that it does not require the specification of a spatial weights matrix. An extensive Monte Carlo simulation confirms the good properties of the scan-LM test in terms of size and power. This test is also robust in the presence of non-normality and other forms of a spatial heteroskedasticity. We finally propose an application on housing prices in the agglomeration of Madrid for a specific submarket: the attics.

JEL-codes: C21 C52 C63 R15 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00168-019-00919-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:anresc:v:64:y:2020:i:2:d:10.1007_s00168-019-00919-w

Ordering information: This journal article can be ordered from
http://link.springer.com/journal/168

DOI: 10.1007/s00168-019-00919-w

Access Statistics for this article

The Annals of Regional Science is currently edited by Martin Andersson, E. Kim and Janet E. Kohlhase

More articles in The Annals of Regional Science from Springer, Western Regional Science Association Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().