Inference on Co-Integration Parameters in Heteroskedastic Vector Autoregressions
Inference on Co-Integration Parameters in Heteroskedastic Vector Autoregressions
复制标题
异方差向量自回归中协整参数的推断
DOI：
10.2139/ssrn.2359482
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Taylor
中科院分区：
文献类型：
--
作者：
H. Boswijk;Giuseppe Cavaliere;Anders Rahbek;A. Taylor
我们考虑了冲击驱动的向量自回归中的协整关系系数和调整系数的估计和假设检验，它表现出相当一般和未知形式的条件和无条件异方差。我们证明了Johansen(1996)关于同方差下的极大似然估计和相关的似然比检验的传统结果在异方差下一般不成立。因此，对这些系数的标准可信区间和假设检验可能是不可靠的。讨论了基于Wald检验(使用方差矩阵的“三明治”估计器)和使用野生自助法的解决方案。这些不需要从业者为波动性指定参数模型。我们建立了这些方法渐近有效的条件。蒙特卡罗模拟研究表明，在异方分布和同分布环境下，Bootstrap方法比相应的渐近检验方法在有限样本容量上都有显著的改善。对美国利率期限结构的应用说明了关于协整向量和调整系数假设的标准推论和自举推论之间的差异。
We consider estimation and hypothesis testing on the coefficients of the co-integrating relations and the adjustment coefficients in vector autoregressions driven by shocks which display both conditional and unconditional heteroskedasticity of a quite general and unknown form. We show that the conventional results in Johansen (1996) for the maximum likelihood estimators and associated likelihood ratio tests derived under homoskedasticity do not in general hold under heteroskedasticity. As a result, standard confidence intervals and hypothesis tests on these coefficients are potentially unreliable. Solutions based on Wald tests (using a “sandwich” estimator of the variance matrix) and on the use of the wild bootstrap are discussed. These do not require the practitioner to specify a parametric model for volatility. We establish the conditions under which these methods are asymptotically valid. A Monte Carlo simulation study demonstrates that significant improvements in finite sample size can be obtained by the bootstrap over the corresponding asymptotic tests in both heteroskedastic and homoskedastic environments. An application to the term structure of interest rates in the US illustrates the difference between standard and bootstrap inferences regarding hypotheses on the co-integrating vectors and adjustment coefficients.