Сравнение на методи

	Байесов модел на ARMA	Векторна авторегресия (VAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1970s–1980s	1980
Създател	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	Christopher A. Sims
Тип	Bayesian time series model	Multivariate time-series model
Основополагащ източник	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
Други названия	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	VAR, VAR model, vector autoregressive model, multivariate autoregression
Свързани	6	5
Резюме	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.