2022/11/04 by Xiao Huang, Huang, Xiao
Computer Science · Mathematics · #Econometrics (econ.EM) #FOS: Economics and business #Neural Networks and Applications #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2211.02215
openalex publication_date 2022/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.