방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 공간 패널 데이터 모형 (고정효과/확률효과)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 공간분석 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2014 | 2019 |
| 창시자≠ | Elhorst; Lee & Yu | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Spatial econometric panel model | Linear regression |
| 원전≠ | 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 |
| 별칭 | 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 |
| 관련≠ | 4 | 5 |
| 요약≠ | 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). |
| ScholarGate데이터셋 ↗ |
|
|