auto_hpfj: Automatic selection of the optimal HP filter with jumps

In jumps: Hodrick-Prescott Filter with Jumps

Automatic selection of the optimal HP filter with jumps

Description

The regularization constant for the HP filter with jumps is the maximal sum of standard deviations for the level disturbance. This value has to be passed to the hpfj function. The auto_hpfj runs hpfj on a grid of regularization constants and returns the output of hpfj selected by the chosen information criterion.

Usage

auto_hpfj(
  y,
  grid = seq(0, sd(y, na.rm = TRUE) * 10, sd(y, na.rm = TRUE)/10),
  ic = c("bic", "hq", "aic", "aicc"),
  edf = TRUE
)

Arguments

y	numeric vector cotaining the time series;
grid	numeric vector containing the grid for the argument maxsum of the hpfj function;
ic	string with information criterion for the choice: the default is "bic" (simulations show this is the best choice), but also "hq", "aic" and "aicc" are available;
edf	logical scalar: TRUE (default) if the number of degrees of freedom should be computed as "effective degrees of freedom" (Efron, 1986) as opposed to a more traditional way (although not supported by theory) when FALSE.

Value

The same ouput as the hpjf function corresponding to the best choice according to the selected information criterion.

Examples

mod <- auto_hpfj(Nile)
plot(as.numeric(Nile))
lines(mod$smoothed_level)

jumps documentation built on April 4, 2025, 2:22 a.m.