Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Globaali spatiaalinen Durbin-malli (SDM)× | Globaali spatiaalinen virhemalli (SEM)× | |
|---|---|---|
| Tieteenala | Spatiaalianalyysi | Spatiaalianalyysi |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2009 | 1988 |
| Kehittäjä≠ | Durbin (1960); adapted to spatial context by LeSage & Pace (2009) | Luc Anselin |
| Tyyppi | Spatial regression model | Spatial regression model |
| Alkuperäislähde≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322 |
| Rinnakkaisnimet | SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag | SEM, spatial error model, spatial error regression, global SEM |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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. |
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