Trend-Cycle Estimation using the Henderson Filter — henderson

Estimates the trend-cycle component using Henderson moving averages.

Usage

henderson_smoothing(
  x,
  endpoints = c("Musgrave", "QL", "CQ", "CC", "DAF", "CN"),
  length = NULL,
  icr = NULL,
  local_icr = FALSE,
  asymmetric_var = FALSE,
  degree = 3,
  ...
)

Arguments

x: An input time series (object of class "ts" or "mts").
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.
local_icr: Logical. If TRUE, the I/C ratio is estimated locally (as described in Quartier-la-Tente, 2024) instead of globally.
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.
...: Additional arguments passed to rjd3filters::lp_filter().

Value

An object of class c("tc_estimates", "henderson"). See tc_estimates() for a full description of the returned object.

References

Henderson, R. (1916). Note on Graduation by Adjusted Average. Transactions of the Actuarial Society of America 17: 43-48.

Musgrave, J. (1964). A Set of End Weights to End All End Weights. US Census Bureau [Custodian]. https://www.census.gov/library/working-papers/1964/adrm/musgrave-01.html.

Quartier-la-Tente, A. (2024). Improving Real-Time Trend Estimates Using Local Parametrization of Polynomial Regression Filters. Journal of Official Statistics, 40(4), 685-715. doi:10.1177/0282423X241283207 .

Examples

if (FALSE) { # rjd3jars::check_java_version(silent = TRUE)
x <- cars_registrations
tc <- henderson_smoothing(x)
plot(tc, xlim = c(2022, 2025))
}