Limit Theory for M-Estimates in an Integrated Infinite Variance
研究了当观测数据来自具有无限方差新息的积分线性过程时，自回归参数M估计的极限分布，发现M估计渐近地比最小二乘估计高效得多且条件渐近正态。
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.