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

Source: R/spec_regarima.R
add_usrdefvar.Rd

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.

Value

The modified specification (with new user-defined variables)

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/

See also

set_tradingdays, intervention_variable

Examples


# Creating one or several external regressors (TS objects),
# which will be gathered in one or several groups
iv1 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01"
)
iv2 <- intervention_variable(
    frequency = 12,
    start = c(2000, 1),
    length = 60,
    starts = "2001-01-01",
    ends = "2001-12-01",
    delta = 1
)

# Using one variable in a a seasonal adjustment process
# Regressors as a list of two groups reg1 and reg2
vars <- list(
    reg1 = list(x = iv1),
    reg2 = list(x = iv2)
)

# Creating the modelling context
my_context <- modelling_context(variables = vars)

# Customize a default specification
init_spec <- x13_spec_default

# 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)