Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел с произволни ефекти за панелни данни× | Обикновен метод на най-малките квадрати (Pooled OLS) за панелни данни× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1978 | 2010 |
| Създател≠ | Baltagi (textbook treatment); Hausman specification test | Jeffrey Wooldridge (treatment) |
| Тип≠ | Panel data regression | Linear regression on stacked panel observations |
| Основополагащ източник≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8 |
| Други названия | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | Pooled OLS, Pooled Ordinary Least Squares, Simple Panel OLS, Havuzlanmış EKK |
| Свързани≠ | 5 | 2 |
| Резюме≠ | The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data. | Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity. |
| ScholarGateНабор от данни ↗ |
|
|