مقایسهٔ روشها
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| مدل خطای فضایی سراسری (SEM)× | رگرسیون حداقل مربعات معمولی (OLS)× | |
|---|---|---|
| حوزه≠ | تحلیل فضایی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1988 | 2019 |
| پدیدآور≠ | Luc Anselin | Wooldridge (textbook treatment); classical least squares |
| نوع≠ | Spatial regression model | Linear regression |
| منبع بنیادین≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| نامهای دیگر | SEM, spatial error model, spatial error regression, global SEM | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| مرتبط | 5 | 5 |
| خلاصه≠ | 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. | 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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