Self-Weighted Least Absolute Deviation Estimation for Infinite Variance Autoregressive Models
无限方差自回归模型的自加权最小绝对偏差估计
Summary How to undertake statistical inference for infinite variance autoregressive models has been a long-standing open problem. To solve this problem, we propose a self-weighted least absolute deviation estimator and show that this estimator is asymptotically normal if the density of errors and its derivative are uniformly bounded. Furthermore, a Wald test statistic is developed for the linear restriction on the parameters, and it is shown to have non-trivial local power.…