标题： 带有分段局部平稳误差的Bootstrap辅助单位根检验 显示英文标题

标题： Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors

摘要： 在单位根检验中，采用分段局部平稳过程来适应具有二阶或更高阶性质平滑和突变变化的非平稳误差。 在此框架下，传统单位根检验统计量的极限零分布被推导出来，并显示包含多个未知参数。 为避免直接一致估计的困难，我们提出使用相关野生自助法来近似非枢轴极限零分布，并为自助法的一致性提供严格的理论证明。 通过有限样本模拟将所提出的方法与重新着色的野生自助程序进行比较，该方法是为服从异方差线性过程的误差而开发的。 此外，自回归筛子重新着色与相关野生自助法的结合被证明表现良好。 首次在非平稳设置中证明了相关野生自助法的有效性，展示了扩展到与其他局部平稳过程相关的其他推断问题的可能性。 显示英文摘要

摘要： In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order properties. Under this framework, the limiting null distributions of the conventional unit root test statistics are derived and shown to contain a number of unknown parameters. To circumvent the difficulty of direct consistent estimation, we propose to use the dependent wild bootstrap to approximate the non-pivotal limiting null distributions and provide a rigorous theoretical justification for bootstrap consistency. The proposed method is compared through finite sample simulations with the recolored wild bootstrap procedure, which was developed for errors that follow a heteroscedastic linear process. Further, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in a nonstationary setting is demonstrated for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.