方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯自回归条件异方差模型× | 贝叶斯GARCH模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1982 (ARCH); 1989 (Bayesian estimation) | 1989–2000 |
| 提出者≠ | Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989) | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) |
| 类型≠ | Volatility model with Bayesian inference | Bayesian volatility model |
| 开创性文献≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗ |
| 别名 | Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model |
| 相关≠ | 6 | 4 |
| 摘要≠ | The Bayesian ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH. | The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics. |
| ScholarGate数据集 ↗ |
|
|