| 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.
a ts object
More information on auxiliary variables in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/
modelling_context, add_usrdefvar
iv1 <- intervention_variable(
frequency = 12,
start = c(2000, 1),
length = 60,
starts = "2001-01-01",
ends = "2001-12-01"
)
plot(iv1)
iv2 <- intervention_variable(
frequency = 12,
start = c(2000, 1),
length = 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 <- x13_spec_default
new_spec <- add_usrdefvar(init_spec, name = "reg1.iv1", regeffect = "Trend")
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