Random parameters and spatial heterogeneity using Rchoice in R
M Sarrias - REGION, 2020 - region.wu.ac.at
REGION, 2020•region.wu.ac.at
This study focus on models with spatially varying coefficients using simulations. As shown by
Sarrias (2019), this modeling strategy is intended to complement the existing approaches by
using variables at micro level and by adding flexibility and realism to the potential domain of
the coefficient on the geographical space. Spatial heterogeneity is modelled by allowing the
parameters associated with each observed variable to vary “randomly” across space
according to some distribution. To show the main advantages of this modeling strategy, the …
Sarrias (2019), this modeling strategy is intended to complement the existing approaches by
using variables at micro level and by adding flexibility and realism to the potential domain of
the coefficient on the geographical space. Spatial heterogeneity is modelled by allowing the
parameters associated with each observed variable to vary “randomly” across space
according to some distribution. To show the main advantages of this modeling strategy, the …
Abstract
This study focus on models with spatially varying coefficients using simulations. As shown by Sarrias (2019), this modeling strategy is intended to complement the existing approaches by using variables at micro level and by adding flexibility and realism to the potential domain of the coefficient on the geographical space. Spatial heterogeneity is modelled by allowing the parameters associated with each observed variable to vary “randomly” across space according to some distribution. To show the main advantages of this modeling strategy, the Rchoice package in R is used.
region.wu.ac.at
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