标题： 增强的HP滤波器比你想象的更通用 显示英文标题

标题： The boosted HP filter is more general than you might think

摘要： 全球金融危机和新冠衰退重新引发了关于宏观经济数据中趋势-周期发现的讨论，而提升方法最近将流行的HP滤波器升级为一种适合数据丰富和快速计算环境的现代机器学习设备。 本文将提升方法的趋势确定能力扩展到更高阶的积分过程以及根接近单位的时序数据。 该理论是通过理解提升方法对简单指数函数的渐近影响来建立的。 鉴于FRED数据库中存在各种动态模式的时序数据，提升方法能够及时捕捉危机中的衰退和随后的复苏。 显示英文摘要

摘要： The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP 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.