Add a User-Defined Variable to Pre-Processing Specification.

Function allowing to add any user-defined regressor to a specification and allocate its effect to a selected component, excepted to the calendar component. To add user-defined calendar regressors, set_tradingdays. Once added to a specification, the external regressor(s) will also have to be added to a modelling context before being used in an estimation process. see modelling_context and example.

Usage

add_usrdefvar(
  x,
  group = "r",
  name,
  label = paste0(group, ".", name),
  lag = 0,
  coef = NULL,
  regeffect = c("Undefined", "Trend", "Seasonal", "Irregular", "Series",
    "SeasonallyAdjusted")
)

Arguments

x: the specification to customize, must be a "SPEC" class object (see details).
group, name: the name of the regressor in the format "group.name", by default "r.name" by default if group NULL "group.name" has to be the same as in modelling_context (see examples)
label: the label of the variable to be displayed when printing specification or results. By default equals to group.name.
lag: integer defining if the user-defined variable should be lagged. By default (lag = 0), the regressor \(x_t\) is not lagged. If lag = 1, then \(x_{t-1}\) is used.
coef: the coefficient, if needs to be fixed.
regeffect: component to which the effect of the user-defined variable will be assigned. By default ("Undefined"), see details.

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()). Components to which the effect of the regressor can be allocated:

"Undefined" : the effect of the regressor is assigned to an additional component, the variable is used to improve the pre-processing step, but is not removed from the series for the decomposition.
"Trend": after the decomposition the effect is allocated to the trend component, like a Level-Shift
"Irregular": after the decomposition the effect is allocated to the irregular component, like an Additive-outlier.
"Seasonal": after the decomposition the effect is allocated to the seasonal component, like a Seasonal-outlier
"Series": after the decomposition the effect is allocated to the raw series: \(yc_t=y_t+ effect\)
"SeasonallyAdjusted": after the decomposition the effect is allocated to the seasonally adjusted series: \(sa_t=T+I+effect\)

References

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

Examples

# creating one or several external regressors (TS objects),
# which will be gathered in one or several groups
iv1 <- intervention_variable(12, c(2000, 1), 60,
    starts = "2001-01-01", ends = "2001-12-01"
)
iv2 <- intervention_variable(12, c(2000, 1), 60,
    starts = "2001-01-01", ends = "2001-12-01", delta = 1
)
# configuration 1: regressors in the same default group (named "r")
variables <- list("iv1" = iv1, "iv2" = iv2)
# to use those regressors, input : name=r.iv1 and r.iv2 in add_usrdefvar function
# configuration 2: group names are user-defined
# here: regressors as a list of two groups (lists) reg1 and reg2
vars <- list(reg1 = list(iv1 = iv1), reg2 = list(iv2 = iv2))
# to use those regressors, input : name=reg1.iv1 and name=reg2.iv2 in add_usrdefvar function
# creating the modelling context
my_context <- modelling_context(variables = vars)
# customize a default specification
# init_spec <- rjd3x13::x13_spec("RSA5c")
# regressors have to be added one by one
# new_spec<- add_usrdefvar(init_spec,name = "reg1.iv1", regeffect="Trend")
# new spec<- add_usrdefvar(new_spec,name = "reg2.iv2", regeffect="Trend", coef=0.7)
# modelling context is needed for the estimation phase
# sa_x13<- rjd3x13::x13(ABS$X0.2.09.10.M, new_spec, context = my_context)