Abstract
This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran’s I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.
Highlights
In recent years spatial econometric methods gained in popularity
It is possible to check the data with graphical tools like Moran Scatterplots for outliers or local spatial clusters
We have implemented a filtering procedure based on the Gi statistic, which is another measure for local spatial dependence
Summary
In recent years spatial econometric methods gained in popularity. The widely used econometric software package EViews does not contain functions for spatial data analysis. For users of the spatial econometrics toolbox by James P. LeSage the MATLAB3 version of the functions is an appropriate extension. The programs calculate the global and local Moran’s I statistic for spatial autocorrelation and its moments using the normal approximation and the more accurate saddlepoint approximation by Tiefelsdorf (2002). It is possible to check the data with graphical tools like Moran Scatterplots for outliers or local spatial clusters. We have implemented a filtering procedure based on the Gi statistic, which is another measure for local spatial dependence. The user has an uncomplicated possibility to handle the presence of spatial autocorrelation in the data.
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