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>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: Matrix, mboost, tseries
Suggests: data.table, ggplot2, knitr, rmarkdown, scales, strucchange, testthat (≥ 3.0.0), usethis, xts, zoo
Published: 2026-05-27
DOI: 10.32614/CRAN.package.MacroFilters (may not be active yet)
Author: Michal Kinel ORCID iD [aut, cre]
Maintainer: Michal Kinel <michal.kinel at gmail.com>
BugReports: https://github.com/michal0091/MacroFilters/issues
License: MIT + file LICENSE
URL: https://github.com/michal0091/MacroFilters, https://michal0091.github.io/MacroFilters/
NeedsCompilation: no
Language: en-US
Citation: MacroFilters citation info
Materials: README
CRAN checks: MacroFilters results

Documentation:

Reference manual: MacroFilters.html , MacroFilters.pdf
Vignettes: Introduction to MacroFilters (source, R code)
Solving the End-Point Problem in Real-Time (source, R code)
Hyperparameter Tuning for the MacroBoost Hybrid Filter (source, R code)

Downloads:

Package source: MacroFilters_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: MacroFilters_0.1.0.zip, r-oldrel: not available
macOS binaries: r-release (arm64): MacroFilters_0.1.0.tgz, r-oldrel (arm64): MacroFilters_0.1.0.tgz, r-release (x86_64): MacroFilters_0.1.0.tgz, r-oldrel (x86_64): MacroFilters_0.1.0.tgz

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