# The boosted HP filter is more general than you might think

arXiv: https://arxiv.org/abs/2209.09810 · HTML: https://mlsanity.com/paper/2209.09810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [High-Dimensional Granger Causality Tests with an Application to VIX and News](https://mlsanity.com/paper/1912.06307/) (2019)
- [Nuclear Norm Regularized Estimation of Panel Regression Models](https://mlsanity.com/paper/1810.10987/) (2018)
- [Boosting High Dimensional Predictive Regressions with Time Varying Parameters](https://mlsanity.com/paper/1910.03109/) (2019)

## Built on by (most cited first, 2 of 2)

- [On LASSO for High Dimensional Predictive Regression](https://mlsanity.com/paper/2212.07052/) (2022)
- [Econometrics of Machine Learning Methods in Economic Forecasting](https://mlsanity.com/paper/2308.10993/) (2023)

## Abstract (arXiv)

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.