Small‐sample confidence intervals for multivariate impulse response functions at long horizons
针对高持续性变量在长预测期置信区间覆盖不足的问题，提出一种基于局部单位根渐近理论的方法，在长预测期有更好的覆盖性质，并可用于实证分析。
Abstract Existing methods for constructing confidence bands for multivariate impulse response functions may have poor coverage at long lead times when variables are highly persistent. The goal of this paper is to propose a simple method that is not pointwise and that is robust to the presence of highly persistent processes. We use approximations based on local‐to‐unity asymptotic theory, and allow the horizon to be a fixed fraction of the sample size. We show that our method has better coverage properties at long horizons than existing methods, and may provide different economic conclusions in empirical applications. We also propose a modification of this method which has good coverage properties at both short and long horizons. Copyright © 2006 John Wiley & Sons, Ltd.