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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesiansk vektorkorrigeringsmodell (Bayesian VECM)× | Bayesiansk VAR-modell (BVAR)× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2002–2005 | 1984 |
| Upphovsperson≠ | Kleibergen & Paap; Villani | Doan, Litterman & Sims |
| Typ≠ | Bayesian multivariate time series model | Multivariate time-series model |
| Ursprungskälla≠ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. DOI ↗ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Alias | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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. | 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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