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首页> 外文期刊>Econometrica >BOOTSTRAP TESTING OF HYPOTHESES ON CO-INTEGRATION RELATIONS IN VECTOR AUTOREGRESSIVE MODELS
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BOOTSTRAP TESTING OF HYPOTHESES ON CO-INTEGRATION RELATIONS IN VECTOR AUTOREGRESSIVE MODELS

机译：向量自回归模型中假设的共整合关系的自举测试

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摘要

It is well known that the finite-sample properties of tests of hypotheses on the co-integrating vectors in vector autoregressive models can be quite poor, and that current solutions based on Bartlett-type corrections or bootstrap based on unrestricted parameter estimators are unsatisfactory, in particular in those cases where also asymptotic (2) tests fail most severely. In this paper, we solve this inference problem by showing the novel result that a bootstrap test where the null hypothesis is imposed on the bootstrap sample is asymptotically valid. That is, not only does it have asymptotically correct size, but, in contrast to what is claimed in existing literature, it is consistent under the alternative. Compared to the theory for bootstrap tests on the co-integration rank (Cavaliere, Rahbek, and Taylor, 2012), establishing the validity of the bootstrap in the framework of hypotheses on the co-integrating vectors requires new theoretical developments, including the introduction of multivariate Ornstein-Uhlenbeck processes with random (reduced rank) drift parameters. Finally, as documented by Monte Carlo simulations, the bootstrap test outperforms existing methods.
机译：众所周知，矢量自回归模型中对协整矢量的假设检验的有限样本性质可能会很差，并且基于Bartlett型校正或基于无限制参数估计量的Bootstrap的当前解决方案在以下方面是不令人满意的：特别是在渐近（2）检验也最严重失败的情况下。在本文中，我们通过显示一个新颖的结果来解决这个推理问题，即在引导样本上施加零假设的引导检验是渐近有效的。也就是说，它不仅具有渐近正确的大小，而且与现有文献中所主张的相反，在替代方案下是一致的。与基于协整秩的自举检验理论相比（Cavaliere，Rahbek和Taylor，2012），在协整向量假设框架内建立自举的有效性需要新的理论发展，包括引入具有随机（降低的秩）漂移参数的多变量Ornstein-Uhlenbeck过程。最后，正如蒙特卡洛模拟所证明的那样，自举测试的性能优于现有方法。

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