Testing for spatial error dependence in probit models
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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.
KeywordsSpatial econometrics Spatial probit Moran’s I
JEL ClassificationC21 C25
This project was supported by Award No. 2009-SQ-B9-K101 by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect those of the Department of Justice.
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