Сравнение на методи

	Байесов тест на Грейнджър за причинност	Байесов модел за корекция на грешки във векторна форма (Bayesian VECM)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1969 (frequentist); 1984 (Bayesian treatment)	2002–2005
Създател	Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature	Kleibergen & Paap; Villani
Тип	Bayesian causal inference test	Bayesian multivariate time series model
Основополагащ източник	Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier.	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.
Други названия	Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Свързани	6	5
Резюме	Bayesian Granger causality tests whether past values of one time series carry predictive information about another, framing the hypothesis through Bayesian inference rather than frequentist p-values. It combines a vector autoregressive (VAR) structure with prior distributions over coefficients and evaluates causal claims via posterior probabilities or Bayes factors, providing a probabilistic and nuanced alternative to the classical Granger test.	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.