Comparar métodos

	Modelo Vetorial de Correção de Erros Bayesiano (VECM Bayesiano)	Modelo de Vetor Autoregressivo Bayesiano (BVAR)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	2002–2005	1984
Autor original	Kleibergen & Paap; Villani	Doan, Litterman & Sims
Tipo	Bayesian multivariate time series model	Multivariate time-series model
Fonte seminal	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Outros nomes	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Relacionados	5	5
Resumo	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.