方法对比
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| 稳健自回归滑动平均模型× | 稳健移动平均（MA）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≠ | 1986 | 1979–2009 |
| 提出者≠ | Martin & Yohai (1986); broader robust time series literature | Denby & Martin (1979); Muler, Pena & Yohai (2009) |
| 类型 | Robust time series model | Robust 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 ↗ | Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146. DOI ↗ |
| 别名 | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation | robust MA, robust moving average, M-estimation MA, bounded-influence MA |
| 相关≠ | 5 | 6 |
| 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 Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA. |
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