论文标题
增强的HP过滤器比您想象的要笼统
The boosted HP filter is more general than you might think
论文作者
论文摘要
全球金融危机和共同衰退已重新讨论有关宏观经济数据中趋势周期发现的讨论，而促进最近将流行的HP过滤器升级为适合数据富含数据且快速计算环境的现代机器学习设备。本文将Boosting的趋势确定能力扩展到了高阶集成过程和时间序列，并与本地与统一的根源有关。通过理解增强对简单指数函数的渐近效应来建立该理论。鉴于FRED数据库中表现出各种动态模式的时间序列的宇宙，可以及时捕捉到随后的危机和恢复性的下降。
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.
