Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Mínimos Quadrados Ponderados Bayesianos (Bayesian WLS)× | Modelo Bayesiano de Efeitos Fixos× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1971 | 2000–2008 |
| Autor original≠ | Arnold Zellner (Bayesian econometrics framework) | Chib (2008); Lancaster (2000) |
| Tipo≠ | Bayesian weighted regression | Bayesian panel regression |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | 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 |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | 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. |
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