Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de Error Espacial Global (SEM)× | Modelo Global de Durbin Espacial (SDM)× | |
|---|---|---|
| Campo | Análisis espacial | Análisis espacial |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1988 | 2009 |
| Autor original≠ | Luc Anselin | Durbin (1960); adapted to spatial context by LeSage & Pace (2009) |
| Tipo | Spatial regression model | Spatial regression model |
| Fuente seminal≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322 | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 |
| Alias | SEM, spatial error model, spatial error regression, global SEM | SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag |
| Relacionados | 5 | 5 |
| Resumen≠ | The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations. | The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region. |
| ScholarGateConjunto de datos ↗ |
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