Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель панельных данных с фиксированными эффектами× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2005 | 2019 |
| Автор метода≠ | Baltagi (textbook treatment); Hausman test for FE vs RE choice | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Panel data regression | Linear regression |
| Основополагающий источник≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | within estimator, panel fixed effects, entity fixed effects model, Panel Sabit Etkiler Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | The fixed effects panel model estimates relationships in panel data (many units observed over time) by exploiting only the within-unit variation, so that unobserved time-invariant heterogeneity is controlled away. It is the central within estimator developed in Baltagi's Econometric Analysis of Panel Data (2005), and the choice between it and the random effects model is settled by the Hausman (1978) test. | 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). |
| ScholarGateНабор данных ↗ |
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