Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська регресія "квантиль-на-квантиль"× | Байєсівська модель векторної авторегресії (BVAR)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2015–2019 | 1984 |
| Автор методу≠ | Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001) | Doan, Litterman & Sims |
| Тип≠ | Nonparametric quantile regression with Bayesian estimation | Multivariate time-series model |
| Основоположне джерело≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8. 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 QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Bayesian Quantile-on-Quantile (BQQ) Regression extends the Sim-Zhou quantile-on-quantile framework by replacing frequentist local linear estimation with Bayesian posterior inference. For each pair of quantiles (theta of the outcome, tau of the predictor), the method yields a full posterior distribution over the slope, enabling uncertainty quantification across the entire bivariate quantile surface — a key advantage when sample sizes are moderate and tail quantiles are sparse. | 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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