Boosted p-Values for High-Dimensional Vector Autoregression

Xiao Huang

Papers from arXiv.org

Abstract: 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.

Date: 2022-11, Revised 2023-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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