Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models
提出基于Bootstrap-after-Bootstrap的Bonferroni预测区间，用于自回归模型。蒙特卡洛模拟表明，该方法在小样本下优于渐近和标准Bootstrap预测区间，尤其当模型存在单位根或近单位根时，能更准确评估未来不确定性。
The use of the Bonferroni prediction interval based on the bootstrap-after-bootstrap is proposed for autoregressive (AR) models. Monte Carlo simulations are conducted using a number of AR models including stationary, unit-root, and near-unit-root processes. The major finding is that the bootstrap-after-bootstrap provides a superior small-sample alternative to asymptotic and standard bootstrap prediction intervals. The latter are often too narrow, substantially underestimating future uncertainty, especially when the model has unit roots or near unit roots. Bootstrap-after-bootstrap prediction intervals are found to provide accurate and conservative assessment of future uncertainty under nearly all circumstances considered.