Salīdzināt metodes

	Bayesiešu VAR modelis (BVAR)	Beijesa vektora kļūdu korekcijas modelis (Beijesa VECM)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1984	2002–2005
Autors	Doan, Litterman & Sims	Kleibergen & Paap; Villani
Tips	Multivariate time-series model	Bayesian multivariate time series model
Pirmavots	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.
Citi nosaukumi	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Saistītās	5	5
Kopsavilkums	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.	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.