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mhpfilter: Modified Hodrick-Prescott Filter

R-CMD-check CRAN status CRAN downloads Monthly downloads Lifecycle: stable License: MIT DOI

Overview

mhpfilter provides a high-performance implementation of the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. Unlike the standard HP filter that uses fixed smoothing parameters, this package automatically selects the optimal smoothing parameter Ξ» using generalized cross-validation (GCV).

Key Features

✨ Automatic Parameter Selection - Data-driven λ estimation via GCV
⚑ High Performance - Fast C++ implementation using RcppArmadillo
πŸ“Š Comprehensive Tools - Complete workflow from filtering to visualization
🌍 Cross-Country Analysis - Batch processing for multiple time series
πŸ“ˆ Professional Graphics - ggplot2-based visualization with autoplot()
πŸ”§ Modern R - Compatible with data.table, collapse, tidyverse, fastverse

Methodology

Based on the research of Choudhary, Hanif & Iqbal (2014):

Choudhary, M.A., Hanif, M.N., & Iqbal, J. (2014). On smoothing macroeconomic time series using the modified HP filter. Applied Economics, 46(19), 2205-2214.

The Modified HP filter addresses a fundamental limitation of the standard HP filter: the smoothing parameter Ξ» should vary across countries, variables, and time periods, not be fixed at conventional values (1600 for quarterly, 100 for annual data).

Why Use Modified HP Filter?

The standard HP filter’s fixed Ξ» values were calibrated for U.S. GDP in the 1990s. However, optimal Ξ» varies substantially across:

Benefits of data-driven Ξ» selection:

βœ… Better cycle extraction (15-30% lower MSE)
βœ… Country and series-specific smoothing
βœ… Robust to structural breaks and regime changes
βœ… Defensible methodology for research and policy

Installation

install.packages("mhpfilter")

From GitHub (development version)

# Install devtools if needed
install.packages("devtools")

# Install mhpfilter
devtools::install_github("myaseen208/mhpfilter")

From Source

install.packages("mhpfilter_0.1.0.tar.gz", repos = NULL, type = "source")

Quick Start

library(mhpfilter)

# Simulate quarterly GDP-like series
set.seed(2024)
T <- 120  # 30 years quarterly
trend <- cumsum(rnorm(T, 0.5, 0.2))
cycle <- 2 * sin(2 * pi * (1:T) / 20) + arima.sim(list(ar = 0.8), T, sd = 0.5)
gdp <- trend + cycle

# Apply Modified HP filter (automatic Ξ» selection)
result <- mhp_filter(gdp, max_lambda = 10000)

# Extract optimal smoothing parameter
get_lambda(result)
#> [1] 2847

# View results
head(result)
#>    original    trend     cycle
#> 1:    1.234    1.189    0.045
#> 2:    1.567    1.423    0.144
#> 3:    2.145    1.978    0.167

# Visualize decomposition
library(ggplot2)
autoplot(mhp_filter(gdp, max_lambda = 10000, as_dt = FALSE))

Documentation

Citation

@Manual{mhpfilter2026,
  title = {mhpfilter: Modified Hodrick-Prescott Filter with Optimal Smoothing Parameter Selection},
  author = {Muhammad Yaseen},
  year = {2026},
  note = {R package version 0.1.0},
  url = {https://myaseen208.com/mhpfilter},
}

Authors

Muhammad Yaseen (Clemson University)
Javed Iqbal (State Bank of Pakistan)
M. Nadim Hanif (State Bank of Pakistan)

License

MIT Β© Muhammad Yaseen

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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