Comparer des méthodes

	Régression bayésienne des moindres carrés ordinaires (Bayesian OLS)	Modèle bayésien à effets aléatoires
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1971	1972–1995
Auteur d'origine	Arnold Zellner	Lindley & Smith (1972); extended by Gelman, Rubin and colleagues
Type	Bayesian linear regression	Bayesian hierarchical panel model
Source fondatrice	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares	Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM
Apparentées	5	5
Résumé	Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small.	The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable.