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| Modello MA Robusto (MA)× | Modello ARIMA Robusto× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1979–2009 | 1986–1993 |
| Ideatore≠ | Denby & Martin (1979); Muler, Pena & Yohai (2009) | Tsay (1986); Chen & Liu (1993) |
| 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 ↗ | Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141. DOI ↗ |
| Alias | robust MA, robust moving average, M-estimation MA, bounded-influence MA | robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection |
| Correlati≠ | 6 | 4 |
| 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. | Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA. |
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