Journal of Economic Literature (JEL) Code(s)
C22, C55, C43
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.
Mei, Ziwei; Phillips, Peter C. B.; and Shi, Zhentao, "The boosted HP filter is more general than you might think" (2022). Cowles Foundation Discussion Papers. 2747.