View source: R/spec_benchmarking.R
set_benchmarking | R Documentation |
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.
set_benchmarking(
x,
enabled = NA,
target = c(NA, "CalendarAdjusted", "Original"),
rho = NA,
lambda = NA,
forecast = NA,
bias = c(NA, "None")
)
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 ( |
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 ( |
bias |
TODO |
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()
).
More information on benchmarking in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/
# 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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.