HETEROSKEDASTIC TIME SERIES WITH A UNIT ROOT
研究了当创新过程存在非平稳随机波动率时，Dickey-Fuller类统计量的极限分布，并提出了基于野自助法的推断方法，蒙特卡洛模拟显示该方法在有限样本中表现良好。
In this paper we provide a unified theory, and associated invariance principle, for the large-sample distributions of the Dickey–Fuller class of statistics when applied to unit root processes driven by innovations displaying nonstationary stochastic volatility of a very general form. These distributions are shown to depend on both the spot volatility and the integrated variation associated with the innovation process. We propose a partial solution (requiring any leverage effects to be asymptotically negligible) to the identified inference problem using a wild bootstrap–based approach. Results are initially presented in the context of martingale differences and are later generalized to allow for weak dependence. Monte Carlo evidence is also provided that suggests that our proposed bootstrap tests perform very well in finite samples in the presence of a range of innovation processes displaying nonstationary volatility and/or weak dependence.