One‐step M‐estimators in the linear model, with dependent errors

We consider the problem of robust M‐estimation of a vector of regression parameters, when the errors are dependent. We assume a weakly stationary, but otherwise quite general dependence structure. Our model allows for the representation of the correlations of any time series of finite length. We first construct initial estimates of the regression, scale, and autocorrelation parameters. The initial autocorrelation estimates are used to transform the model to one of approximate independence. In this transformed model, final one‐step M‐estimates are calculated. Under appropriate assumptions, the regression estimates so obtained are asymptotically normal, with a variance‐covariance structure identical to that in the case in which the autocorrelations are known a priori. The results of a simulation study are given. Two versions of our estimator are compared with the L 1 ‐estimator and several Huber‐type M‐estimators. In terms of bias and mean squared error, the estimators are generally very close. In terms of the coverage probabilities of confidence intervals, our estimators appear to be quite superior to both the L 1 ‐estimator and the other estimators. The simulations also indicate that the approach to normality is quite fast.