Publication Type
Journal Article
Version
submittedVersion
Publication Date
5-2021
Abstract
We propose a procedure of iterating the HP filter to produce a smarter smoothing device, called the boosted HP (bHP) filter, based on L2-boosting in machine learning. Limit theory shows that the bHP filter asymptotically recovers trend mechanisms that involve integrated processes, deterministic drifts, and structural breaks, covering the most common trends that appear in current modeling methodology. A stopping criterion automates the algorithm, giving a data-determined method for data-rich environments. The methodology is illustrated in simulations and with three real data examples that highlight the differences between simple HP filtering, the bHP filter, and an alternative autoregressive approach.
Keywords
Boosting, Cycles, Empirical macroeconomics, Hodrick-Prescott filter, Machine learning, Nonstationary time series, Trends, Unit root processes
Discipline
Econometrics
Research Areas
Econometrics
Publication
International Economic Review
Volume
62
Issue
2
First Page
521
Last Page
570
ISSN
0020-6598
Identifier
10.1111/iere.12495
Publisher
Wiley
Citation
PHILLIPS, Peter C. B. and SHI, Zhentao.
Boosting: Why you can use the HP filter. (2021). International Economic Review. 62, (2), 521-570.
Available at: https://ink.library.smu.edu.sg/soe_research/2821
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/iere.12495