方法对比
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| 贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)× | 向量自回归 (VAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002 (DCC); 2000s (Bayesian extension) | 1980 |
| 提出者≠ | Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards) | Christopher A. Sims |
| 类型≠ | Multivariate volatility model | Multivariate time-series model |
| 开创性文献≠ | 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. DOI ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| 别名 | Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. |
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