9 Citations (Scopus)

Abstract

In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the differences between the tests proposed by Pinkse and Slade (J Econom 85(1):125-254, 1998), Pinkse (Asymptotics of the Moran test and a test for spatial correlation in Probit models, 1999; Advances in Spatial Econometrics, 2004) and Kelejian and Prucha (J Econom 104(2):219-257, 2001), and compare their properties in a extensive set of Monte Carlo simulation experiments both under the null and under the alternative. We also assess the conjecture by Pinkse (Asymptotics of the Moran test and a test for spatial correlation in Probit models, 1999) that the usefulness of these test statistics is limited when the explanatory variables are spatially correlated. The Kelejian and Prucha (J Econom 104(2):219-257, 2001) generalized Moran's I statistic turns out to perform best, even in medium sized samples of several hundreds of observations. The other two tests are acceptable in very large samples.

Original languageEnglish (US)
Pages (from-to)91-101
Number of pages11
JournalLetters in Spatial and Resource Sciences
Volume6
Issue number2
DOIs
StatePublished - Jul 2013

Fingerprint

statistics
Testing
Probit model
test
econometrics
autocorrelation
simulation
experiment
Test statistic
Spatial correlation
Simulation experiment
Autocorrelation
Statistics
Monte Carlo simulation
Usefulness
Moran's I
Spatial econometrics

Keywords

  • Moran's I
  • Spatial econometrics
  • Spatial probit

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Demography
  • Urban Studies
  • Economics and Econometrics

Cite this

Testing for spatial error dependence in probit models. / Amaral, Pedro V.; Anselin, Luc; Arribas-Bel, Daniel.

In: Letters in Spatial and Resource Sciences, Vol. 6, No. 2, 07.2013, p. 91-101.

Research output: Contribution to journalArticle

Amaral, Pedro V. ; Anselin, Luc ; Arribas-Bel, Daniel. / Testing for spatial error dependence in probit models. In: Letters in Spatial and Resource Sciences. 2013 ; Vol. 6, No. 2. pp. 91-101.
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