Intervention variable

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.

Usage

intervention_variable(
  frequency,
  start,
  length,
  s,
  starts,
  ends,
  delta = 0,
  seasonaldelta = 0
)

Arguments

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 c(1980, 1)) and number of periods of the output variables. Can also be provided with the s argument
s: time series used to get the dates for the trading days variables. If supplied the parameters frequency, start and length are ignored.
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.

Value

a ts object

Details

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.

References

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

Examples

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")