Compara mètodes

	Regressió Bayesiana Quantil-sobre-Quantil	Model de Vector Autoregressiu Bayesian (BVAR)
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2015–2019	1984
Autor original	Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)	Doan, Litterman & Sims
Tipus	Nonparametric quantile regression with Bayesian estimation	Multivariate time-series model
Font seminal	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Àlies	Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Relacionats	6	5
Resum	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.