Salīdzināt metodes

	Robustais ARCH modelis	Robustā regresija
Nozare	Ekonometrija	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	2002–2008	1964
Autors	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)
Tips	Volatility / conditional heteroscedasticity model	Regression with outlier resistance
Pirmavots	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Citi nosaukumi	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Saistītās	6	6
Kopsavilkums	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.