DETERMINING THE COINTEGRATION RANK IN HETEROSKEDASTIC VAR MODELS OF UNKNOWN ORDER
未知阶异方差 VAR 模型的协整秩确定AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究积分 VAR 的协整秩、滞后阶数与异方差下的统计方法，核心是时间序列建模，并未涉及结构冲击识别。
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未知阶异方差 VAR 模型的协整秩确定
- 研究问题
- 当积分 VAR 系统的滞后阶数未知且冲击存在条件或无条件异方差时，如何同时选择滞后阶数和协整秩，并保持推断有效？
- 方法与思路
- 作者研究序贯及联合信息准则的惩罚条件，并将 Johansen 序贯方法与 wild bootstrap 理论扩展至先一致选择滞后阶的情形。
- 材料陈述的贡献
- 摘要称，适当信息准则可一致选择阶数与协整秩，相关序贯方法在所述异方差设定下仍有效，并修正既有一致性证明的错误。
- 阅读关注点
- 应核对允许的异方差形式、惩罚函数及最大候选阶数条件，并检查先选阶再检验与联合选择各自的理论和模拟比较范围。
- 阅读边界
- 仅据所给 Crossref 版本摘要，不能判断具体惩罚条件或有限样本差异；模拟比较结论不能推广到所有异方差与滞后设定。
原文摘要
We investigate the asymptotic and finite sample properties of a number of methods for estimating the cointegration rank in integrated vector autoregressive systems of unknown autoregressive order driven by heteroskedastic shocks. We allow for both conditional and unconditional heteroskedasticity of a very general form. We establish the conditions required on the penalty functions such that standard information criterion-based methods, such as the Bayesian information criterion [BIC], when employed either sequentially or jointly, can be used to consistently estimate both the cointegration rank and the autoregressive lag order. In doing so we also correct errors which appear in the proofs provided for the consistency of information-based estimators in the homoskedastic case by Aznar and Salvador (2002, Econometric Theory 18, 926–947). We also extend the corpus of available large sample theory for the conventional sequential approach of Johansen (1995, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models . Oxford University Press) and the associated wild bootstrap implementation thereof of Cavaliere, Rahbek, and Taylor (2014, Econometric Reviews 33, 606–650) to the case where the lag order is unknown. In particular, we show that these methods remain valid under heteroskedasticity and an unknown lag length provided the lag length is first chosen by a consistent method, again such as the BIC. The relative finite sample properties of the different methods discussed are investigated in a Monte Carlo simulation study. The two best performing methods in this study are a wild bootstrap implementation of the Johansen (1995, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models . Oxford University Press) procedure implemented with BIC selection of the lag length and joint IC approach (cf. Phillips, 1996, Econometrica 64, 763–812) which uses the BIC to jointly select the lag order and the cointegration rank.