Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo Espaço-Temporal de Erro Espacial× | Regressão Geograficamente Ponderada (GWR)× | |
|---|---|---|
| Área | Análise espacial | Análise espacial |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1988 (SEM); 2003 (panel/space-time extension) | 2002 |
| Autor original≠ | Anselin (1988); panel extension by Elhorst (2003, 2014) | Fotheringham, Brunsdon & Charlton |
| Tipo≠ | Spatial panel regression | Local spatial regression |
| Fonte seminal≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737247 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Outros nomes | SEM panel, spatial error panel model, space-time SEM, spatiotemporal error model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | The Space-Time Spatial Error Model (space-time SEM) is a spatial panel regression technique that accounts for spatial dependence confined to the error term across geographic units and time periods. It corrects biased inference caused by spatially correlated disturbances while estimating covariate effects on a panel of spatial observations. | 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. |
| ScholarGateConjunto de dados ↗ |
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