Simultaneous Spatial and Functional Form Transformations
نویسندگان
چکیده
Parsimonious regression models using locational data often yield non-normal, heteroskedastic, and spatially dependent residuals. This manuscript develops a model which simultaneously performs spatial and functional form transformations to mitigate these problems. We apply the model to 11,006 observations on housing prices in Baton Rouge. For these data, the model reduces the interquartile range of the errors in the untransformed variable’s space by 38.38% relative to a simple model using the untransformed variable. In addition, the pattern of residuals improves dramatically and the generalized additive model exhibits interesting transformations. Finally, the manuscript documents the computational innovations which make it possible to perform the maximization of the likelihood in under 10 seconds.
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