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

Abstract

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.

Model Selection Based on Residual Dependence Measure

“…Without considering the abnormal data, this study intends to fit the Boston housing price dataset using different regression methods, and then evaluate the different models from the perspective of residual dependence measurement. Maryna [24] believed that the spatial correlation test of residuals could reflect whether a model captured the spatial correlation. Therefore, the estimation models with residuals were selected, which were OLS, ModelA, ModelB, SAC, GNS, c-spline and F-spline.…”

“…As can be seen from the above table, excluding the constant terms of the equation estimated by spline interpolation, TABLE 2. The following table shows the fitted equations in [24].…”

Study on the Spatial Convergence Club and Growth Momentum of China’s Regional Economies

“…In this study, exploratory spatial data analysis and a log-t test of a nonlinear timevarying factor model were combined to identify the spatial convergence of clubs; then, a spatial econometric model was used to find the influencing factors of a club's economic growth. Spatial spillover effects depend not only on geographical location [27] but also on regional economic links. Therefore, an exploratory spatial data analysis based on the three spatial weight matrices of adjacency (w1), geographical distance (w2), and economic distance (w3) was conducted to explore the spatial correlations of regional economies.…”

Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

“…Intended future research includes the improvement of the variable selection using lasso or ridge methods and other spatial semi-parametric alternatives [21,22]. Additionally, future research could be the application to model water risk of the interesting study [23], which proposes a conditional range directional distance estimator by modifying the range directional distance model utilising the probabilistic characterisation of directional distance functions (DDF).…”