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Revista Colombiana de Estadística
Article . 2022 . Peer-reviewed
License: CC BY
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Article . 2022
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Spatial Econometric Models: A Bayesian Approach

Spatial econometric models: a Bayesian approach
Authors: Cepeda-Cuervo, Edilberto; Sicacha, Jorge Armando;

Spatial Econometric Models: A Bayesian Approach

Abstract

In this paper we propose Bayesian methods to fit econometric regression models, including those where the variability is assumed to follow a regression structure. We formulate the main functions of the statistical R-package BSPADATA, developed according to the proposed methods to obtain posteriori parameter inferences. After that, we include results of simulated studies to illustrate the use of this package and the performance of the proposed methods. Finally, we provide studies to illustrate the applications of the models and compare our results with that obtained by maximum likelihood.

Related Organizations
Keywords

SAR models, Bayesian methods, Statistics, CAR models, spatial econometric models

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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