Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Ucheleweshaji wa Anga (SAR / Spatial Autoregressive)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Uchanganuzi wa Kimaeneo | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1988 | 2019 |
| Mwanzilishi≠ | Anselin (textbook formalisation); LeSage & Pace | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Spatial autoregressive regression | Linear regression |
| Chanzo asilia≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Majina mbadala | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. | 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). |
| ScholarGateSeti ya data ↗ |
|
|