Decompose a times series via the Hodrick-Prescott filter
Estimate cyclical and trend component with filter by Hodrick and Prescott (1997). The function is based on the function hpfilter from the archived mFilter-package.
hp_filter(x, lambda)
x |
One column matrix with numeric values. |
lambda |
Numeric value. |
A list. The first element contains the cyclical component and the second element the trend component.
Philipp Adämmer
Hodrick, R.J., and Prescott, E. C. (1997). "Postwar U.S. Business Cycles: An Empirical Investigation." Journal of Money, Credit and Banking, 29(1), 1-16.
Ravn, M.O., Uhlig, H. (2002). "On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations." Review of Economics and Statistics, 84(2), 371-376.
library(lpirfs)
# Decompose the Federal Funds Rate
data_set <- as.matrix(interest_rules_var_data$FF)
hp_results <- hp_filter(data_set, 1600)
# Extract results and save as data.frame
hp_cyc <- as.data.frame(hp_results[[1]])
hp_trend <- as.data.frame(hp_results[[2]])
# Make data.frames for plots
cyc_df <- data.frame(yy = hp_cyc$V1, xx = seq(as.Date('1955-01-01'),
as.Date('2003-01-01') , "quarter"))
trend_df <- data.frame(yy = hp_trend$V1, xx = seq(as.Date('1955-01-01'),
as.Date('2003-01-01') , "quarter"))
# Make plots
library(ggplot2)
# Plot cyclical part
ggplot(data = cyc_df) +
geom_line(aes(y = yy, x = xx))
# Plot trend component
ggplot(trend_df) +
geom_line(aes(y = yy, x = xx))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.