Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Bayesian VAR-mudel (BVAR)× | Bayes'i vektor-vektoorne parandusmudel (Bayesian VECM)× | |
|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1984 | 2002–2005 |
| Looja≠ | Doan, Litterman & Sims | Kleibergen & Paap; Villani |
| Tüüp≠ | Multivariate time-series model | Bayesian multivariate time series model |
| Algallikas≠ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. DOI ↗ |
| Rööpnimetused | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. | The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples. |
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