The boosted Hodrick‐Prescott filter is more general than you might think
扩展了增强型Hodrick-Prescott滤波器的趋势识别能力，使其适用于高阶积分过程和近单位根时间序列，并通过FRED数据库中的实际数据验证了其在危机和复苏中的及时捕捉效果。
Summary The global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.