ts_fil_hp {tspredit}

Hodrick-Prescott Filter

Description

This filter eliminates the cyclical component of the series, performs smoothing on it, making it more sensitive to long-term fluctuations. Each observation is decomposed into a cyclical and a growth 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.

Value

a ts_fil_hp object.

Examples

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

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

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