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| Modello MA Robusto (MA)× | Modello ARMA Robusto× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1979–2009 | 1986 |
| Ideatore≠ | Denby & Martin (1979); Muler, Pena & Yohai (2009) | Martin & Yohai (1986); broader robust time series literature |
| Tipo | Robust time series model | Robust time series model |
| Fonte seminale≠ | 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 ↗ | Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1-9. link ↗ |
| Alias | robust MA, robust moving average, M-estimation MA, bounded-influence MA | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | 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. | 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. |
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