BOOSTING: WHY YOU CAN USE THE HP FILTER
提出一种基于机器学习的迭代HP滤波方法（bHP滤波器），能更智能地提取趋势，适用于含单位根、确定性漂移和结构断点的数据，并给出自动停止准则。
Abstract We propose a procedure of iterating the HP filter to produce a smarter smoothing device, called the boosted HP (bHP) filter, based on L 2 ‐boosting in machine learning. Limit theory shows that the bHP filter asymptotically recovers trend mechanisms that involve integrated processes, deterministic drifts, and structural breaks, covering the most common trends that appear in current modeling methodology. A stopping criterion automates the algorithm, giving a data‐determined method for data‐rich environments. The methodology is illustrated in simulations and with three real data examples that highlight the differences between simple HP filtering, the bHP filter, and an alternative autoregressive approach.