Structural break problems in linear regression coefficients are often accompanied by the instabilities of the regressors and/or the serially correlated error term such as structural changes in their marginal distributions. In these circumstances, we find that existing tests as well as Hansen (2000)'s fixed regressor bootstrap have serious size distortions, although the latter is designed to overcome the unstable regressors. To tackle this problem, we propose a method that combines the fixed regressor bootstrap and the sieve wild bootstrap. We show that the sieve wild bootstrap asymptotically replicates a broad set of serially correlated unstable error processes. Using that the fixed regressor bootstrap is designed to capture the instability of the regressors, the mixture of the two is then shown to provide correct asymptotic critical values of the existing tests in various feasible nonstandard cases. Monte Carlo experiments show significant improvements in size and power.

中文翻译：

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当回归变量和与序列相关的误差项不稳定时，进行结构破裂的自举测试

线性回归系数中的结构折断问题通常伴随着回归变量的不稳定性和/或与序列相关的误差项，例如其边际分布中的结构变化。在这种情况下，我们发现现有的测试以及Hansen（2000）的固定回归器引导程序都有严重的尺寸失真，尽管后者旨在克服不稳定的回归器。为了解决这个问题，我们提出了一种结合固定回归器引导程序和筛网野生引导程序的方法。我们表明，筛野生引导程序渐近地复制了一系列广泛的序列相关的不稳定错误过程。使用固定的回归器引导程序来捕获回归器的不稳定性，然后，在各种可行的非标准情况下，将两者的混合物显示为现有测试提供正确的渐近临界值。蒙特卡洛实验表明，尺寸和功率都有了显着改善。