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| Model ARCH odporny na wartości odstające× | Regresja odporna× | |
|---|---|---|
| Dziedzina≠ | Ekonometria | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2002–2008 | 1964 |
| Twórca≠ | Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Typ≠ | Volatility / conditional heteroscedasticity model | Regression with outlier resistance |
| Źródło pierwotne≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Inne nazwy | robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series. | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
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