标题： 协整向量自回归过程的统一推断 显示英文标题

标题： Uniform Inference for Cointegrated Vector Autoregressive Processes

摘要： 协整向量自回归过程的统一有效推断到目前为止由于最小二乘估计量渐近分布中的某些不连续性而显得困难。我们将单变量情况下的渐近结果扩展到多维情况，并展示了如何基于这些结果进行推断。此外，我们还表明，滞后增强和一种最近的工具变量方法也可以产生统一有效的检验和置信区域。我们通过两个具体例子的模拟实验验证了理论结果并研究了有限样本性质。 显示英文摘要

摘要： Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence regions. We verify the theoretical findings and investigate finite sample properties in simulation experiments for two specific examples.