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In the spatial econometrics literature, spatial error dependence is characterized by spatial autoregressive processes, which relate every observation in the cross-section to any other with distance-decaying intensity: i.e., dependence obeys Tobler's First Law of Geography ('everything is related...
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This paper focuses on several estimation methods for SAR- models in case of missing observations in the dependent variable. First, we show with an example and then in general, how missing observations can change the model and thus resulting in the failure of the 'traditional' estimation methods....
Persistent link: https://www.econbiz.de/10012653811
Likelihood functions of spatial autoregressive models with normal but heteroskedastic disturbances have been already derived [Anselin (1988, ch.6)]. But there is no implementation for maximum likelihood estimation of these likelihood functions in general (heteroskedastic disturbances) cases....
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econometric models. We also examine some diagnostic measures to evaluate the spatial autocorrelation of the pseudo … arrangement on the variables in the predictive model and reduce the effect of spatial autocorrelation on prediction errors. In … remove the spatial autocorrelation on pseudo-residuals and improvement in the accuracy of spatial predictive models. …
Persistent link: https://www.econbiz.de/10011532588
autocorrelation of house prices and explanatory variables within larger distances, whereas the significant spatial autocorrelation of …
Persistent link: https://www.econbiz.de/10011495528
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). First, we compute the locational Gini coefficient and Moran's I statistics of global spatial autocorrelation. We show that …
Persistent link: https://www.econbiz.de/10011574931
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