Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Data wa Paneli wa Angani (FE/RE)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Uchanganuzi wa Kimaeneo | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2014 | 2019 |
| Mwanzilishi≠ | Elhorst; Lee & Yu | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Spatial econometric panel model | Linear regression |
| Chanzo asilia≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Majina mbadala | spatial panel FE/RE, spatial econometric panel, spatial lag/error panel, Uzamsal Panel Modeli (Spatial Panel FE/RE) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (2014) and Lee & Yu (2010). | 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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