Robust autoregressive estimates using quadratic programming

The robust estimation of the autoregressive parameters is formulated in terms of the quadratic programming problem. This article's main contribution is to present an estimator that down weights both types of outliers in time series and improves the forecasting results. New robust estimates are yielded, by combining optimally two weight functions suitable for Innovation and Additive outliers in time series. The technique which is developed here is based on an approach of mathematical programming applications to 1p-approximation. The behavior of the estimators is illustrated numerically, under the additive outlier generating model. Monte Carlo results show that the proposed estimators compared favorably with respect to M-estimators and bounded influence estimators. Based on these results we conclude that one can improve the robust properties of AR(p) estimators using quadratic programming.