Порівняння методів

	Модель Байєсівського EGARCH	Байєсівська модель векторної авторегресії (BVAR)
Галузь	Економетрика	Економетрика
Родина	Regression model	Regression model
Рік появи	1991 (EGARCH); 2000s (Bayesian estimation)	1984
Автор методу	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Doan, Litterman & Sims
Тип	Volatility model with Bayesian inference	Multivariate time-series model
Основоположне джерело	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Інші назви	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Пов'язані	6	5
Підсумок	The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.	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.