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Manage Outliers/Ramps in Specification

Source: R/spec_regarima.R
add_outlier.Rd

Generic function to add outliers or Ramp regressors (add_outlier() and add_ramp()) to a specification or to remove them (remove_outlier() and remove_ramp()).

Usage

add_outlier(x, type, date, name = sprintf("%s (%s)", type, date), coef = 0)

remove_outlier(x, type = NULL, date = NULL, name = NULL)

add_ramp(x, start, end, name = sprintf("rp.%s - %s", start, end), coef = 0)

remove_ramp(x, start = NULL, end = NULL, name = NULL)

Arguments

x

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

type, date

type and date of the outliers. Possible type are: "AO" = additive, "LS" = level shift, "TC" = transitory change and "SO" = seasonal outlier.

name

the name of the variable (to format print).

coef

the coefficient if needs to be fixed. If equal to 0 the outliers/ramps coefficients are estimated.

start, end

dates of the ramp regressor.

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()). If a Seasonal adjustment process is performed, each type of Outlier will be allocated to a pre-defined component after the decomposition: "AO" and "TC" to the irregular, "LS" and Ramps to the trend.

References

More information on outliers and other auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See also

add_usrdefvar, intervention_variable

Examples

# init_spec <- rjd3x13::x13_spec("RSA5c")
# new_spec<-rjd3toolkit::add_outlier(init_spec, type="AO", date="2012-01-01")
# ramp on year 2012
# new_spec<-rjd3toolkit::add_ramp(init_spec,start="2012-01-01",end="2012-12-01")