Boosted p-Values for High-Dimensional Vector Autoregression

• Tác giả: Xiao Huang
• Ngôn ngữ: eng
• Ký hiệu phân loại: 519.5 Statistical mathematics
• Thông tin xuất bản: 2022
• Bộ sưu tập: Metadata
• ID: 196030

Tóm tắt

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.