ts_fil_hp: Hodrick-Prescott Filter

In tspredit: Time Series Prediction with Integrated Tuning

Hodrick-Prescott Filter

Description

Decompose a series into trend and cyclical components using the Hodrick–Prescott (HP) filter and optionally blend with the original series.

This filter removes short-term fluctuations by penalizing changes in the growth rate of the trend component.

Usage

ts_fil_hp(lambda = 100, preserve = 0.9)

Arguments

lambda

It is the smoothing parameter of the Hodrick-Prescott filter. Lambda = 100*(frequency)^2 Correspondence between frequency and lambda values annual => frequency = 1 // lambda = 100 quarterly => frequency = 4 // lambda = 1600 monthly => frequency = 12 // lambda = 14400 weekly => frequency = 52 // lambda = 270400 daily (7 days a week) => frequency = 365 // lambda = 13322500 daily (5 days a week) => frequency = 252 // lambda = 6812100

preserve

value between 0 and 1. Balance the composition of observations and applied filter. Values close to 1 preserve original values. Values close to 0 adopts HP filter values.

Details

The filter strength is governed by lambda = 100 * frequency^2. Use preserve in (0, 1] to convex-combine the raw series and the HP trend.

Value

A ts_fil_hp object.

References

• R. J. Hodrick and E. C. Prescott (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1).

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_hp(lambda = 100*(26)^2)  #frequency assumed to be 26
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

tspredit documentation built on Feb. 11, 2026, 9:08 a.m.