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| Modello ARIMA Robusto× | Regressione quantilica× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1986–1993 | 1978 |
| Ideatore≠ | Tsay (1986); Chen & Liu (1993) | Koenker & Bassett |
| Tipo≠ | Robust time series model | Conditional quantile regression |
| Fonte seminale≠ | Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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