set_outlier: Set Outlier Detection Parameters

In palatej/rjd3toolkit: Utility Functions around 'JDemetra+ 3.0'

Set Outlier Detection Parameters

Description

Function allowing to customize the automatic outlier detection process built in in the pre-processing step (regarima or tramo)

Usage

set_outlier(
  x,
  span.type = c(NA, "All", "From", "To", "Between", "Last", "First", "Excluding"),
  d0 = NULL,
  d1 = NULL,
  n0 = 0,
  n1 = 0,
  outliers.type = NA,
  critical.value = NA,
  tc.rate = NA,
  method = c(NA, "AddOne", "AddAll"),
  maxiter = NA,
  lsrun = NA,
  eml.est = NA
)

Arguments

x	the specification to customize, must be a "SPEC" class object (see details).
span.type, d0, d1, n0, n1	parameters to specify the sub-span on which outliers will be detected. d0 and d1 characters in the format "YYYY-MM-DD" to specify first/last date of the span when type equals to "From", "To" or "Between". n0 and n1 numerics to specify the number of periods at the beginning/end of the series to be used for the span (type equals to "From", "To") or to exclude (type equals to "Excluding").
outliers.type	vector of characters of the outliers to be automatically detected. "AO" for additive outliers, "TC" for transitory changes "LS" for level shifts and "SO" for seasonal outliers. For example outliers.type = c("AO", "LS") to enable the detection of additive outliers and level shifts. If outliers.type = NULL or outliers.type = character(), automatic detection of outliers is disabled. Default value = outliers.type = c("AO", "LS", "TC")
critical.value	numeric. Critical value for the outlier detection procedure. If equal to 0 the critical value is automatically determined by the number of observations in the outlier detection time span.(Default value = 4 REGARIMA/X13 and 3.5 in TRAMO)
tc.rate	the rate of decay for the transitory change outlier (Default = 0.7).
method	(REGARIMA/X13 Specific) determines how the program successively adds detected outliers to the model. Currently, only the "AddOne" method is supported.
maxiter	(REGARIMA/X13 Specific) maximum number of iterations (Default = 30).
lsrun	(REGARIMA/X13 Specific) number of successive level shifts to test for cancellation (Default = 0).
eml.est	(TRAMO Specific) logical for the exact likelihood estimation method. It controls the method applied for parameter estimation in the intermediate steps. If TRUE, an exact likelihood estimation method is used. When FALSE, the fast Hannan-Rissanen method is used.

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" to the trend and "SO" to seasonal component.

References

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

See Also

add_outlier, add_usrdefvar

Examples

# init_spec <- rjd3tramoseats::spec_tramoseats("rsafull")
# new_spec<-set_outlier(init_spec, span.type= "From", d0 = "2012-01-01",
#                      outliers.type = c("LS", "AO"),
#                      critical.value = 5,
#                      tc.rate =0.85)

palatej/rjd3toolkit documentation built on Oct. 30, 2024, 10:46 p.m.