Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська векторна модель корекції помилок (Bayesian VECM)× | Байєсівська модель векторної авторегресії (BVAR)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2002–2005 | 1984 |
| Автор методу≠ | Kleibergen & Paap; Villani | Doan, Litterman & Sims |
| Тип≠ | Bayesian multivariate time series model | Multivariate time-series model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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