方法对比
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| 稳健自回归模型× | 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≠ | 1986 | 1970 |
| 提出者≠ | Martin & Yohai (influential early work); broader robust time series literature | George E. P. Box and Gwilym M. Jenkins |
| 类型≠ | Robust time series model | Time series model |
| 开创性文献≠ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| 相关≠ | 6 | 5 |
| 摘要≠ | The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics. | 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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