Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский взвешенный метод наименьших квадратов (Bayesian WLS)× | Модель Байесовских фиксированных эффектов× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1971 | 2000–2008 |
| Автор метода≠ | Arnold Zellner (Bayesian econometrics framework) | Chib (2008); Lancaster (2000) |
| Тип≠ | Bayesian weighted regression | Bayesian panel regression |
| Основополагающий источник≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. ISBN: 978-0471169376 | Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413. DOI ↗ |
| Другие названия | Bayesian weighted regression, BWLS, Bayesian heteroscedastic regression, weighted Bayesian linear regression | Bayesian within estimator, Bayesian FE model, Bayesian individual fixed effects, Bayesian least squares dummy variable |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Bayesian Weighted Least Squares combines the classical WLS weighting scheme — which downweights observations with high error variance — with Bayesian prior distributions over the regression coefficients and error variance. The result is a posterior distribution that reflects both the data likelihood and prior beliefs, providing full uncertainty quantification in heteroscedastic settings. | The Bayesian fixed effects model applies Bayesian inference to the classical within-group panel estimator. Unit-specific intercepts capture time-invariant unobserved heterogeneity, while prior distributions on all parameters allow probability statements about coefficients and full uncertainty quantification via the posterior distribution. |
| ScholarGateНабор данных ↗ |
|
|