Function allowing to customize the automatic outlier detection process built in in the pre-processing step (regarima or tramo)
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
andd1
characters in the format "YYYY-MM-DD" to specify first/last date of the span whentype
equals to"From"
,"To"
or"Between"
.n0
andn1
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 exampleoutliers.type = c("AO", "LS")
to enable the detection of additive outliers and level shifts. Ifoutliers.type = NULL
oroutliers.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. IfTRUE
, an exact likelihood estimation method is used. WhenFALSE
, 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/