论文标题
单变量和多变量自回归时间序列模型的强大引导预测间隔
Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models
论文作者
论文摘要
Bootstrap过程已成为一个通用框架，用于构建自回归时间序列模型中未来观察的预测间隔。具有外围数据点的这种模型在实际数据应用中是标准的，尤其是在计量经济学领域。这些偏远的数据点倾向于产生较高的预测错误，从而减少基于非舒适估计器计算的现有自举预测间隔的预测性能。在单变量和多元自回归时间序列中，我们提出了一种可靠的自举算法，用于构建预测间隔和预测区域。所提出的程序基于加权似然估计和加权残差。通过一系列蒙特卡洛研究和两个经验数据示例检查其有限样本特性。
The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples.
