View source: R/spec_regarima.R
| add_usrdefvar | R Documentation |
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.
add_usrdefvar(
x,
group = "r",
name,
label = paste0(group, ".", name),
lag = 0,
coef = NULL,
regeffect = c("Undefined", "Trend", "Seasonal", "Irregular", "Series",
"SeasonallyAdjusted")
)
x |
the specification to customize, must be a "SPEC" class object (see details). |
group, name |
the name of the regressor in the format |
label |
the label of the variable to be displayed when printing specification or results. By default equals to |
lag |
integer defining if the user-defined variable should be lagged.
By default ( |
coef |
the coefficient, if needs to be fixed. |
regeffect |
component to which the effect of the user-defined variable will be assigned.
By default ( |
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
The modified specification (with new user-defined variables)
More information on outliers and other auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/
set_tradingdays, intervention_variable
# 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)
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