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MacroFilters: Robust Trend-Cycle Decomposition for Macroeconomic Time Series
Provides high-performance tools for macroeconomic trend extraction and filtering, specifically designed to solve the end-point problem in real-time. Implements the MacroBoost Hybrid (MBH) filter using penalized P-splines and gradient boosting. Unlike the standard Hodrick-Prescott filter, 'MacroFilters' utilizes component-wise L2-boosting with robust loss functions (Huber) to handle extreme transient shocks (e.g., COVID-19) without inducing spurious trend shifts. The algorithm includes an automated two-layer diagnostic stage for unit roots and structural breaks, optimized via corrected AICc for computational efficiency. Methodology detailed in Kinel (2026) <doi:10.2139/ssrn.6371138>.
Package details |
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| Author | Michal Kinel [aut, cre] (ORCID: <https://orcid.org/0009-0007-3295-7199>) |
| Maintainer | Michal Kinel <michal.kinel@gmail.com> |
| License | MIT + file LICENSE |
| Version | 0.2.1 |
| URL | https://github.com/michal0091/MacroFilters https://michal0091.github.io/MacroFilters/ |
| Package repository | View on CRAN |
| Installation |
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