Confronta i metodi

	Regressione Bayesiana Quantile-su-Quantile	Modello Bayesiano a Correzione d'Errore Vettoriale (Bayesian VECM)
Campo	Econometria	Econometria
Famiglia	Regression model	Regression model
Anno di origine	2015–2019	2002–2005
Ideatore	Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)	Kleibergen & Paap; Villani
Tipo	Nonparametric quantile regression with Bayesian estimation	Bayesian multivariate time series model
Fonte seminale	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.
Alias	Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Correlati	6	5
Sintesi	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 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.