ABSTRACT

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.

摘要

自回归时间序列模型中，bootstrap 程序已成为构建未来观察预测区间的通用框架。这种具有异常数据点的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。这些外围数据点往往会产生高预测误差，这会降低基于非稳健估计器计算的现有引导预测区间的预测性能。在单变量和多变量自回归时间序列中，我们提出了一种鲁棒的引导算法来构建预测区间和预测区域。建议的程序基于加权似然估计和加权残差。它的有限样本属性通过一系列蒙特卡罗研究和两个经验数据示例进行检查。