Salīdzināt metodes

	Beijesa vektora kļūdu korekcijas modelis (Beijesa VECM)	Bayesiešu ARIMA modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2002–2005	1970s (ARIMA); Bayesian extension prominent from 1990s
Autors	Kleibergen & Paap; Villani	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)
Tips	Bayesian multivariate time series model	Bayesian time series model
Pirmavots	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.	Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903
Citi nosaukumi	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model
Saistītās	5	6
Kopsavilkums	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 ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.