增强型HP滤波器比你想象的更具普适性
The boosted HP filter is more general than you might think
AI总结：
针对宏观经济趋势-周期识别需求，本文将boosting增强的HP滤波器拓展至高阶单整与近单位根时间序列，通过指数函数渐近分析建立理论，并在FRED数据集上验证其可及时捕捉危机下滑与复苏。
摘要：
全球金融危机与新冠疫情引发的经济衰退重新引发了关于宏观经济数据中趋势-周期识别的讨论，而boosting（提升）技术近期将流行的HP滤波器升级为适用于数据丰富、高速计算环境的现代机器学习工具。本文将boosting的趋势判定能力拓展至高阶单整过程以及根接近单位根的时间序列，通过分析boosting对简单指数函数的渐近效应建立了相关理论。在FRED数据库中涵盖各类动态模式的时间序列全集上，boosting及时捕捉到了危机期间的经济下滑及后续的复苏过程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
