Salīdzināt metodes

	Robustā kustīgo vidēju (MA) modelis	Robustā OLS (OLS ar robustām standarta kļūdām)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1979–2009	1980
Autors	Denby & Martin (1979); Muler, Pena & Yohai (2009)	Halbert White
Tips	Robust time series model	Linear regression with robust inference
Pirmavots	Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Citi nosaukumi	robust MA, robust moving average, M-estimation MA, bounded-influence MA	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Saistītās	6	6
Kopsavilkums	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 OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.