Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Baijesa OLS (Baijesa parastā mazāko kvadrātu regresija)× | Bayesiešu VAR modelis (BVAR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1971 | 1984 |
| Autors≠ | Arnold Zellner | Doan, Litterman & Sims |
| Tips≠ | Bayesian linear regression | Multivariate time-series model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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