intervention_variable | R Documentation |
Function allowing to create external regressors as sequences of zeros and ones. The generated variables
will have to be added with add_usrdefvar
function will require a modelling context definition
with modelling_context
to be used in an estimation process.
intervention_variable(
frequency,
start,
length,
s,
starts,
ends,
delta = 0,
seasonaldelta = 0
)
frequency |
Frequency of the series, number of periods per year (12,4,3,2..) |
start , length |
First date (array with the first year and the first period)
(for instance |
s |
time series used to get the dates for the trading days variables. If supplied the
parameters |
starts , ends |
characters specifying sequences of starts/ends dates for the intervention variable. Can be characters or integers. |
delta |
regular differencing order. |
seasonaldelta |
seasonal differencing order. |
Intervention variables are combinations of any possible sequence of ones and zeros
(the sequence of ones being defined by the parameters starts
and ends
)
and of \frac{1}{(1-B)^d}
and \frac{1}{(1-B^s)^D}
where B
is the
backwards operator, s
is the frequency of the time series,
d
and D
are the parameters delta
and seasonaldelta
.
For example, with delta = 0
and seasonaldelta = 0
we get temporary level shifts defined
by the parameters starts
and ends
. With delta = 1
and seasonaldelta = 0
we get
the cumulative sum of temporary level shifts, once differenced the regressor will become a classical level shift.
More information on auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/
modelling_context
, add_usrdefvar
iv1 <- intervention_variable(12, c(2000, 1), 60,
starts = "2001-01-01", ends = "2001-12-01"
)
plot(iv1)
iv2 <- intervention_variable(12, c(2000, 1), 60,
starts = "2001-01-01", ends = "2001-12-01", delta = 1
)
plot(iv2)
# 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 <- rjd3x13::x13_spec("RSA5c")
# new_spec<- add_usrdefvar(init_spec,id = "reg1.iv1", regeffect="Trend")
# modelling context is needed for the estimation phase
# sa_x13<- rjd3x13::x13(ABS$X0.2.09.10.M, new_spec, context = my_context)
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