Asymptotics of empirical processes of long memory moving averages with infinite variance

This paper obtains a uniform reduction principle for the empirical process of a stationary moving average time series {Xt} with long memory and independent and identically distributed innovations belonging to the domain of attraction of symmetric [alpha]-stable laws, 1<[alpha]<2. As a consequence, an appropriately standardized empirical process is shown to converge weakly in the uniform-topology to a degenerate process of the form f Z, where Z is a standard symmetric [alpha]-stable random variable and f is the marginal density of the underlying process. A similar result is obtained for a class of weighted empirical processes. We also show, for a large class of bounded functions h, that the limit law of (normalized) sums [summation operator]s=1nh(Xs) is symmetric [alpha]-stable. An application of these results to linear regression models with moving average errors of the above type yields that a large class of M-estimators of regression parameters are asymptotically equivalent to the least-squares estimator and [alpha]-stable. This paper thus extends various well-known results of Dehling-Taqqu and Koul-Mukherjee from finite variance long memory models to infinite variance models of the above type.