Regression modelEconometrics / time series

Robust Moving Average (MA) Model

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.

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Sources

  1. 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: 10.1080/01621459.1979.10481631
  2. Muler, N., Pena, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. Annals of Statistics, 37(2), 816–840. DOI: 10.1214/07-AOS570

Related methods

Referenced by

ScholarGateRobust MA model (Robust Moving Average Model). Retrieved 2026-06-04 from https://scholargate.app/en/econometrics/robust-ma-model