论文标题
提高高维矢量自重力的p值
Boosted p-Values for High-Dimensional Vector Autoregression
论文作者
论文摘要
评估参数估计值的统计显着性是高维矢量自动估计建模的重要步骤。使用最小二乘的提升方法，我们在线性模型中的每个提升步骤中计算每个选定参数的p值。 P值在渐近上有效，并且还适用于增强程序的迭代性质。我们的仿真实验表明，P值可以在高维矢量自动加注中控制假阳性率。在具有100多个宏观经济时间序列的应用程序中，我们进一步表明，P值不仅可以选择具有良好预测性能的稀疏模型，还可以帮助控制模型稳定性。开发了一个伴侣r包boostvar。
Assessing the statistical significance of parameter estimates is an important step in high-dimensional vector autoregression modeling. Using the least-squares boosting method, we compute the p-value for each selected parameter at every boosting step in a linear model. The p-values are asymptotically valid and also adapt to the iterative nature of the boosting procedure. Our simulation experiment shows that the p-values can keep false positive rate under control in high-dimensional vector autoregressions. In an application with more than 100 macroeconomic time series, we further show that the p-values can not only select a sparser model with good prediction performance but also help control model stability. A companion R package boostvar is developed.
