2015
DOI: 10.18637/jss.v063.i18
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Comparing Implementations of Estimation Methods for Spatial Econometrics

Abstract: Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up-to-date comparison of generalized method of moments and maximum likelihood implementations now available. The c… Show more

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Cited by 739 publications
(517 citation statements)
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“…32,33 Spatial models control for spatial dependence among the variables (ie, that people live near people who are like them). This allows us to understand how factors such as the percentage of students who are white are correlated with PBE percentages without the inflating effect of spatial autocorrelation.…”
Section: Methodsmentioning
confidence: 99%
“…32,33 Spatial models control for spatial dependence among the variables (ie, that people live near people who are like them). This allows us to understand how factors such as the percentage of students who are white are correlated with PBE percentages without the inflating effect of spatial autocorrelation.…”
Section: Methodsmentioning
confidence: 99%
“…Using the Durbin-Watson test, we found that autocorrelation did not affect the residuals. The Moran's I test from the spdep-package (Bivand et al 2013;Bivand and Piras 2015) detected no spatial autocorrelation in the residuals. For this test, we defined neighbors as those cells that touch each other along the border.…”
Section: Diagnostics and Robustness Checksmentioning
confidence: 92%
“…Although different interpretations of the literature as well as on the choice of techniques when implementing spatial econometrics lead to some differences in the results (Bivand and Piras 2015), results in Table 3 reveal no differences in the estimated coefficients and t statistics between MATLAB and R.…”
Section: Testing Spatial Autocorrelationmentioning
confidence: 95%