BOOTSTRAP UNIT ROOT TESTS FOR TIME SERIES WITH NONSTATIONARY VOLATILITY
针对宏观经济和金融变量中常见的永久性波动变化，提出一种基于wild bootstrap的单位根检验方法，无需指定波动率参数模型，在有限样本中表现良好。
The presence of permanent volatility shifts in key macroeconomic and financial variables in developed economies appears to be relatively common. Conventional unit root tests are unreliable in the presence of such behavior, having nonpivotal asymptotic null distributions. In this paper we propose a bootstrap approach to unit root testing that is valid in the presence of a wide class of permanent variance changes that includes single and multiple (abrupt and smooth transition) volatility change processes as special cases. We make use of the so-called wild bootstrap principle, which preserves the heteroskedasticity present in the original shocks. Our proposed method does not require the practitioner to specify any parametric model for the volatility process. Numerical evidence suggests that the bootstrap tests perform well in finite samples against a range of nonstationary volatility processes.We thank two anonymous referees, Paulo Rodrigues, Peter Phillips, and seminar participants at the URCT conference held in Faro, Portugal, September 29 to October 1, 2005, for helpful comments on previous versions of this paper.