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摘要翻译：
在单位根检验中，采用分段局部平稳过程来适应二阶或高阶性质既有平滑变化又有突变的非平稳误差。在此框架下，导出了常规单位根检验统计量的极限零分布，并证明了该分布包含许多未知参数。为了克服直接相合估计的困难，我们提出了用相依野生bootstrap来逼近非关键极限零分布，并为bootstrap的相合性提供了严格的理论依据。通过有限样本模拟，将所提出的方法与recolored wild bootstrap程序进行了比较，该程序是针对异方差线性过程中的误差而开发的。此外，自回归筛重色与相关的野生自举的组合显示出良好的性能。首次证明了相依野生引导在非平稳环境下的有效性，并给出了推广到与局部平稳过程相关的其它推理问题的可能性。
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英文标题：
《Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary
Errors》
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作者：
Yeonwoo Rho and Xiaofeng Shao
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1802.05333
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