标题： 提升的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疫情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工具。 本文揭示了其在趋势-周期确定中的灵活性，以简单的方式解释了HP滤波器平滑以及提升方法在一般趋势检测中提供的一致性。 应用于FRED数据库中的时间序列集合，提升方法在及时捕捉危机期间的衰退和随后的复苏方面优于其他方法。 由于其广泛的应用性，提升的HP滤波器是宏观计量经济学工具箱中有用的自动化机器学习补充。 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 sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit.