Multi-Method Trend-Cycle Estimation — smoothing • publishTC

Computes trend-cycle estimates using multiple smoothing methods simultaneously and returns a list of "tc_estimates" objects.

Usage

smoothing(
  x,
  methods = c("henderson", "henderson_localic", "henderson_robust",
    "henderson_robust_localic", "clf_cn", "clf_alf"),
  endpoints = "Musgrave",
  length = NULL,
  icr = NULL,
  asymmetric_var = FALSE,
  degree = 3,
  ao = NULL,
  ao_tc = NULL,
  ls = NULL,
  ...
)

Arguments

x: An input time series (object of class "ts" or "mts").
methods: A list of character strings specifying the smoothing methods to apply. Defaults to c("henderson", "henderson_localic", "clf").
endpoints: Character string specifying the method used for asymmetric filters. By default, "musgrave" is used.
length: An integer specifying the length of the symmetric filter. If NULL, the length is selected automatically using x11_trend_selection().
icr: Numeric value specifying the Irregular-to-Trend (I/C) ratio used for asymmetric filters.
asymmetric_var: Logical. When local_icr = TRUE, if TRUE, the variance is estimated for each asymmetric filter. If FALSE (the default), the variance associated with symmetric estimates is used throughout.
degree: Integer. When local_icr = TRUE, degree of the polynomial used to estimate the local bias parameter.
ao: Vector of dates for Additive Outliers (AO) whose effects are allocated to the irregular component.
ao_tc: Vector of dates for Additive Outliers (AO) whose effects are allocated to the trend-cycle component.
ls: Vector of dates for Level Shifts (LS) whose effects are allocated to the trend-cycle component.
...: Additional parameters passed to the underlying smoothing functions.

Value

A named list of objects of class "tc_estimates", where each element corresponds to one of the specified methods in methods.

Details

This convenience function applies multiple trend-cycle estimation algorithms to the same input series, facilitating direct comparison between different filtering approaches (e.g., standard Henderson, Henderson with local I/C ratio, or CLF smoothing).

Examples

if (FALSE) { # rjd3jars::check_java_version(silent = TRUE)
x <- french_ipi[, "manufacturing"]
outliers <- x13_regarima_outliers(x)
all_methods <- smoothing(x, ao = outliers$ao, ao_tc = outliers$ao_tc, ls = outliers$ls)

plot(all_methods$henderson, xlim = c(2023, 2025))
lines(all_methods$henderson_localic, col_tc = "blue")
}