方法对比
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| 稳健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≠ | 1986 | 1970 |
| 提出者≠ | Martin & Yohai (1986); 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≠ | Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1-9. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| 相关 | 5 | 5 |
| 摘要≠ | The Robust ARMA model extends the classical Autoregressive Moving Average framework by replacing the sensitive least-squares loss with outlier-resistant estimation methods — typically M-estimators or median-based approaches. This protects coefficient estimates and forecasts from being distorted by additive outliers, level shifts, or innovational outliers that are common in economic and financial time series. | 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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