Limit Theory for M-Estimates in an Integrated Infinite Variance
We consider the limiting distributions of M -estimates of an “autoregressive” parameter when the observations come from an integrated linear process with infinite variance innovations. It is shown that M -estimates are, asymptotically, infinitely more efficient than the least-squares estimator (in the sense that they have a faster rate of convergence) and are conditionally asymptotically normal.
Volume (Year): 7 (1991)
Issue (Month): 02 (June)
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