विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्थानिक डर्बिन मॉडल (SDM)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|
| क्षेत्र≠ | स्थानिक विश्लेषण | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2009 | 2019 |
| प्रवर्तक≠ | LeSage & Pace | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Spatial regression model | Linear regression |
| मौलिक स्रोत≠ | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| उपनाम≠ | SDM, spatial mixed model, uzamsal durbin modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित | 5 | 5 |
| सारांश≠ | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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