Skip to contents

Set Benchmarking Specification

Source: R/spec_benchmarking.R
set_benchmarking.Rd

Function allowing to perform a benchmarking procedure after the decomposition step in a seasonal adjustment (disabled by default). Here benchmarking refers to a procedure ensuring consistency over the year between seasonally adjusted and raw (or calendar adjusted) data, as seasonal adjustment can cause discrepancies between the annual totals of seasonally adjusted series and the corresponding annual totals of raw (or calendar adjusted) series.

Usage

set_benchmarking(
  x,
  enabled = NA,
  target = c(NA, "CalendarAdjusted", "Original"),
  rho = NA,
  lambda = NA,
  forecast = NA,
  bias = c(NA, "None")
)

Arguments

x

the specification to customize, must be a "SPEC" class object (see details).

enabled

Boolean to enable the user to perform benchmarking.

target

specifies the target series for the benchmarking procedure, which can be the raw series ("Normal"); or the series adjusted for calendar effects ("CalendarAdjusted").

rho

the value of the AR(1) parameter (set between 0 and 1) in the function used for benchmarking. Default =1.

lambda

a parameter in the function used for benchmarking that relates to the weights in the regression equation; it is typically equal to 0, 1/2 or 1.

forecast

Boolean indicating if the forecasts of the seasonally adjusted series and of the target variable (target) are used in the benchmarking computation so that the benchmarking constrain is also applied to the forecasting period.

bias

TODO

Details

x specification parameter must be a JD3_X13_SPEC" class object generated with rjd3x13::x13_spec() (or "JD3_REGARIMA_SPEC" generated with rjd3x13::spec_regarima() or "JD3_TRAMOSEATS_SPEC" generated with rjd3tramoseats::spec_tramoseats() or "JD3_TRAMO_SPEC" generated with rjd3tramoseats::spec_tramo()).

References

More information on benchmarking in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

Examples

# init_spec <- rjd3x13::x13_spec("RSA5c")
# new_spec<- set_benchmarking(init_spec,
#                            enabled = TRUE,
#                            target = "Normal",
#                            rho = 0.8,
#                            lambda = 0.5,
#                            forecast = FALSE,
#                            bias = "None")