Adaptive Estimation in ARCH Models
研究了误差服从平稳参数ARCH(P)过程时回归模型中可识别参数的有效估计，不假设误差条件密度的函数形式，但要求其关于零对称，并证明了均值参数和方差过程可识别参数的自适应可估计性。
We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH( P ) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance process and show that the identifiable parameters of this process are adaptively estimable.