方法对比
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| 稳健自回归滑动平均模型× | 稳健自回归模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 1986 | 1986 |
| 提出者≠ | Martin & Yohai (1986); broader robust time series literature | Martin & Yohai (influential early work); broader robust time series literature |
| 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 ↗ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ |
| 别名 | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR |
| 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 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. |
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