方法对比
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| 贝叶斯自回归滑动平均模型× | 自回归移动平均模型 (ARMA)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1970s–1980s | 1970 |
| 提出者≠ | Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s | George E. P. Box and Gwilym M. Jenkins |
| 类型≠ | Bayesian time series model | Time series model |
| 开创性文献≠ | Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
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