Bootstrap Unit Root Tests in Models with GARCH(1,1) Errors
提出一种在GARCH(1,1)误差模型中使用的自助法单位根检验，证明其渐近有效性，并通过模拟显示其在强异方差下能纠正渐近检验的尺寸扭曲，有限样本性质优良。
This article proposes a bootstrap unit root test in models with GARCH(1, 1) errors and establishes its asymptotic validity under mild moment and distributional restrictions. While the proposed bootstrap test for a unit root shares the power enhancing properties of its asymptotic counterpart (Ling and Li, 2003), it offers a number of important advantages. In particular, the bootstrap procedure does not require explicit estimation of nuisance parameters that enter the distribution of the test statistic and corrects the substantial size distortions of the asymptotic test that occur for strongly heteroskedastic processes. The simulation results demonstrate the excellent finite-sample properties of the bootstrap unit root test for a wide range of GARCH specifications.