Bootstrap Determination of the Co-Integration Rank in Vector Autoregressive Models
Bootstrap Determination of the Co-Integration Rank in Vector Autoregressive Models
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DOI：
10.3982/ecta9099
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发表时间：
2012-07
期刊：
影响因子：
6.1
通讯作者：
Giuseppe Cavaliere;Anders Rahbek;A. Taylor
中科院分区：
文献类型：
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作者：
Giuseppe Cavaliere;Anders Rahbek;A. Taylor
本文讨论了Johansen（1996）的似然比协整秩检验及其序贯秩确定过程的自举实现。Bootstrap样本的构造使用的基础向量自回归（VAR）模型的约束参数估计，在降秩零假设下获得。提供了一个完整的渐近理论，表明与Swensen（2006）中使用VAR模型的无限制和限制估计组合的自助程序不同，所得自助数据为I（1），并且满足零协整秩，而不管真实秩如何。这确保了Bootstrap LR检验的大小渐近正确，并且Bootstrap序贯过程选择比真实秩小的秩的概率收敛到零。蒙特卡洛证据表明，我们的引导程序在实践中工作得很好。
This paper discusses a consistent bootstrap implementation of the likelihood ratio (LR) co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying vector autoregressive (VAR) model that obtain under the reduced rank null hypothesis. A full asymptotic theory is provided that shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co-integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.