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Modelo Espacial Global de Painel×Regressão Geograficamente Ponderada (GWR)×
ÁreaAnálise espacialAnálise espacial
FamíliaRegression modelRegression model
Ano de origem2003-20102002
Autor originalElhorst, J. P.; Lee, L. F. & Yu, J.Fotheringham, Brunsdon & Charlton
TipoSpatial panel regressionLocal spatial regression
Fonte seminalElhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Outros nomesspatial panel model with global weights, global spatial panel regression, spatial panel data model, GSPMGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Relacionados45
ResumoThe Global Spatial Panel Model extends panel data regression by incorporating a global spatial weights matrix that links every location to every other location simultaneously. It jointly accounts for cross-sectional spatial dependence, time-series dynamics, and individual fixed or random effects, making it the standard workhorse for panel data when spatial spillovers operate across the full study region.Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.
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ScholarGateComparar métodos: Global Spatial Panel Model · Geographically Weighted Regression. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare