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Open AccessArticle

Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence

Economia Aplicada y Estadistica, International Doctorate in Economics, Universidad Nacional de Educacion a Distancia, 28015 Madrid, Spain
Entropy 2020, 22(4), 466; https://doi.org/10.3390/e22040466
Received: 3 March 2020 / Revised: 12 April 2020 / Accepted: 15 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Information theory and Symbolic Analysis: Theory and Applications)
This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies. View Full-Text
Keywords: symbolic entropy; deterministic trends; specification symbolic entropy; deterministic trends; specification
MDPI and ACS Style

Makeienko, M. Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence. Entropy 2020, 22, 466.

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