We consider the limiting distributions ofM-estimates of an“autoregressive” parameter when the observationscome from an integrated linear process with infinitevariance innovations. It is shown thatM-estimates are, asymptotically,infinitely more efficient than the least-squaresestimator (in the sense that they have a faster rateof convergence) and are conditionally asymptoticallynormal.