Uniform inference for cointegrated vector autoregressive processes
针对协整向量自回归模型中最小二乘估计渐近分布的不连续性问题，将单变量情形下的渐近结果推广到多维，并证明滞后增广和工具变量方法也能得到统一有效的检验和置信区域，通过模拟实验验证了理论发现。
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.