This paper introduces new methods for constructing prediction intervals using quantile-based techniques. The procedures are developed for both classical (homoscedastic) autoregressive models and modern quantile autoregressive models. They combine quantile estimation with multiplier bootstrap schemes to approximate the sampling variability of coefficient estimates, together with bootstrap replications of future observations. We consider both percentile-based and predictive-root-based constructions. Theoretical results establish the validity and pertinence of the proposed methods. Simulation experiments evaluate their finite-sample performance and show that the proposed methods yield improved coverage properties and computational efficiency relative to existing approaches in the literature. The empirical usefulness of the methods is illustrated through applications to U.S. unemployment rate data and retail gasoline prices.

翻译：本文提出了基于分位数技术构建预测区间的新方法。这些方法针对经典（同方差）自回归模型和现代分位数自回归模型分别进行了开发。它们将分位数估计与乘数自助法方案相结合，以近似系数估计的抽样变异性，同时结合未来观测值的自助法复现。我们考虑了基于百分位数和基于预测根两种构建方式。理论结果证明了所提方法的有效性和适用性。仿真实验评估了其有限样本性能，结果表明相较于文献中现有方法，所提方法具有改进的覆盖特性和计算效率。通过对美国失业率数据和零售汽油价格的应用，展示了这些方法的实证效用。