مقایسهٔ روش‌ها

	مدل شرطی پویا بیزی (Bayesian DCC-GARCH)	خودبازگشتی برداری (VAR)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	2002 (DCC); 2000s (Bayesian extension)	1980
پدیدآور	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Christopher A. Sims
نوع	Multivariate volatility model	Multivariate time-series model
منبع بنیادین	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
نام‌های دیگر	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	VAR, VAR model, vector autoregressive model, multivariate autoregression
مرتبط	6	5
خلاصه	Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH.	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.