regarima_outliers: Outlier Detection with a RegARIMA Model

In rjd3x13: Seasonal Adjustment with 'X-13' in 'JDemetra+' 3.x

Outlier Detection with a RegARIMA Model

Description

Outlier Detection with a RegARIMA Model

Usage

regarima_outliers(
  y,
  order = c(0L, 1L, 1L),
  seasonal = c(0L, 1L, 1L),
  mean = FALSE,
  X = NULL,
  X.td = NULL,
  ao = TRUE,
  ls = TRUE,
  tc = FALSE,
  so = FALSE,
  cv = 0,
  clean = FALSE
)

Arguments

y	the dependent variable (a ts object).
order, seasonal	the orders of the ARIMA model.
mean	Boolean to include or not the mean.
X	user defined regressors (other than calendar).
X.td	calendar regressors.
ao, ls, so, tc	Boolean to indicate which type of outliers should be detected.
cv	numeric. The entered critical value for the outlier detection procedure. If equal to 0 the critical value for the outlier detection procedure is automatically determined by the number of observations.
clean	Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.

Value

a "JD3_REGARIMA_OUTLIERS" object, containing input variables and results

Examples

# estimate model
model <- regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
# print outliers
model$model$variables

rjd3x13 documentation built on July 10, 2026, 9:08 a.m.