We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distribution is a two-dimensional normal distribution concentrated on a one-dimensional ray determined by the characteristic root having the larger absolute value.

翻译：我们证明了具有不同绝对值实特征根的超临界二阶自回归过程（高斯新息）的自回归参数最小二乘估计量的混合收敛性。通过采用适当的随机缩放，使极限分布成为集中于由较大绝对值特征根所确定的一维射线上的二维正态分布。