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| OLS bayesiana (Regressione Lineare Ordinaria Bayesiana)× | Modello VAR Bayesiano (BVAR)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1971 | 1984 |
| Ideatore≠ | Arnold Zellner | Doan, Litterman & Sims |
| Tipo≠ | Bayesian linear regression | Multivariate time-series model |
| Fonte seminale≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Alias | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Correlati | 5 | 5 |
| Sintesi≠ | 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 Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. |
| ScholarGateInsieme di dati ↗ |
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