Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models
异方差 VAR 模型中确定协整秩的自适应信息准则方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究一阶单整变量的 VAR 系统、协整秩及滞后选择，因此对应时间序列和包含 VAR 方法的细分方向。
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异方差 VAR 模型中确定协整秩的自适应信息准则方法
- 研究问题
- 在无条件异方差和非平稳波动率下，如何选择 VAR 的滞后阶数与协整秩，避免滞后误选并利用自适应推断的潜在效率收益？
- 方法与思路
- 作者提出自适应信息准则，既可先选择滞后阶数以配合自助法自适应似然比检验，也可联合选择协整秩与滞后阶数。
- 材料陈述的贡献
- 摘要称，在惩罚项满足标准条件时，两种选择方式均具有弱一致性，并以蒙特卡洛模拟及美国期限结构应用展示潜在收益。
- 阅读关注点
- 阅读时应核对非平稳协方差过程的估计、惩罚项条件、自助法实施，以及联合选择与先选滞后的有限样本表现差异。
- 阅读边界
- 所用版本为 arXiv 预印本，同行评审及期刊接收未核验；摘要未给出完整条件、模拟数值和应用结果，不能认定功效普遍改善。
原文摘要
Standard methods, such as sequential procedures based on Johansen's (pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank of a vector autoregressive (VAR) system of variables integrated of order one can be significantly affected, even asymptotically, by unconditional heteroskedasticity (non-stationary volatility) in the data. Known solutions to this problem include wild bootstrap implementations of the PLR test or the use of an information criterion, such as the BIC, to select the co-integration rank. Although asymptotically valid in the presence of heteroskedasticity, these methods can display very low finite sample power under some patterns of non-stationary volatility. In particular, they do not exploit potential efficiency gains that could be realised in the presence of non-stationary volatility by using adaptive inference methods. Under the assumption of a known autoregressive lag length, Boswijk and Zu (2022) develop adaptive PLR test based methods using a non-parameteric estimate of the covariance matrix process. It is well-known, however, that selecting an incorrect lag length can significantly impact on the efficacy of both information criteria and bootstrap PLR tests to determine co-integration rank in finite samples. We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term. Monte Carlo simulations are used to demonstrate the potential gains from using adaptive methods and an empirical application to the U.S. term structure is provided.