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Set Arima Model Identification in Pre-Processing Specification

Source: R/spec_regarima.R
set_automodel.Rd

Function allowing to customize Arima model identification procedure.

Usage

set_automodel(
  x,
  enabled = NA,
  acceptdefault = NA,
  cancel = NA,
  ub1 = NA,
  ub2 = NA,
  reducecv = NA,
  ljungboxlimit = NA,
  tsig = NA,
  ubfinal = NA,
  checkmu = NA,
  mixed = NA,
  fct = NA,
  balanced = NA,
  amicompare = NA
)

Arguments

x

the specification to customize, must be a "SPEC" class object (see details).

enabled

logical. If TRUE, the automatic modelling of the ARIMA model is enabled. If FALSE, the parameters of the ARIMA model can be specified.

acceptdefault

logical. If TRUE, the default model (ARIMA(0,1,1)(0,1,1)) will be chosen in the first step of the automatic model identification, if the Ljung-Box Q statistics for the residuals are acceptable. No further attempt will be made to identify a better model. Default = FALSE

cancel

numeric cancellation limit. A limit for the AR and the MA roots to be assumed equal. This option is used in the automatic identification of the differencing order. If the difference in moduli of an AR and an MA root (when estimating ARIMA(1,0,1)(1,0,1) models in the second step of the automatic identification of the differencing polynomial) is smaller than cancellation limit, the two roots cancel out. Default = 0.1.

ub1

numeric, the first unit root limit. It is the threshold value for the initial unit root test in the automatic differencing procedure. When one of the roots in the estimation of the ARIMA(2,0,0)(1,0,0) plus mean model, performed in the first step of the automatic model identification procedure, is larger than first unit root limit in modulus, it is set equal to unity. Default = 1.030928.

ub2

numeric, the second unit root limit. When one of the roots in the estimation of the ARIMA(1,0,1)(1,0,1) plus mean model, which is performed in the second step of the automatic model identification procedure, is larger than second unit root limit in modulus, it is checked if there is a common factor in the corresponding AR and MA polynomials of the ARMA model that can be cancelled (see automdl.cancel). If there is no cancellation, the AR root is set equal to unity (i.e. the differencing order changes). Default = 1.136364.

reducecv

numeric, ReduceCV. The percentage by which the outlier critical value will be reduced when an identified model is found to have a Ljung-Box statistic with an unacceptable confidence coefficient. The parameter should be between 0 and 1, and will only be active when automatic outlier identification is enabled. The reduced critical value will be set to (1-ReduceCV)xCV, where CV is the original critical value. Default = 0.14268.

ljungboxlimit

numeric, the Ljung Box limit, setting the acceptance criterion for the confidence intervals of the Ljung-Box Q statistic. If the LjungBox Q statistics for the residuals of a final model is greater than Ljung Box limit, then the model is rejected, the outlier critical value is reduced, and model and outlier identification (if specified) is redone with a reduced value. Default = 0.95.

tsig

numeric, the arma limit. It is the threshold value for t-statistics of ARMA coefficients and the constant term used for the final test of model parsimony. If the highest order ARMA coefficient has a t-value smaller than this value in magnitude, the order of the model is reduced. If the constant term has a t-value smaller than the ARMA limit in magnitude, it is removed from the set of regressors. Default=1.

ubfinal

(REGARIMA/X13 Specific) numeric, final unit root limit. The threshold value for the final unit root test. If the magnitude of an AR root for the final model is smaller than the final unit root limit, then a unit root is assumed, the order of the AR polynomial is reduced by one and the appropriate order of the differencing (non-seasonal, seasonal) is increased. The parameter value should be greater than one. Default = 1.05.

checkmu

(REGARIMA/X13 Specific) logical indicating if the automatic model selection checks the significance of the constant term.

mixed

(REGARIMA/X13 Specific) logical. This variable controls whether ARIMA models with non-seasonal AR and MA terms or seasonal AR and MA terms will be considered in the automatic model identification procedure. If FALSE, a model with AR and MA terms in both the seasonal and non-seasonal parts of the model can be acceptable, provided there are no AR or MA terms in either the seasonal or non-seasonal terms.

fct

(REGARIMA/X13 Specific) numeric. TODO.

balanced

(REGARIMA/X13 Specific) logical If TRUE, the automatic model identification procedure will have a preference for balanced models (i.e. models for which the order of the combined AR and differencing operators is equal to the order of the combined MA operators). Default = FALSE

amicompare

(TRAMO Specific) logical. If TRUE, the program compares the model identified by the automatic procedure to the default model (\(ARIMA(0,1,1)(0,1,1)\)) and the model with the best fit is selected. Criteria considered are residual diagnostics, the model structure and the number of outliers.

Details

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()).

References

More information on reg-arima modelling in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/

See also

set_arima, set_transform

Examples

# init_spec <- rjd3x13::x13_spec("RSA5c")
# new_spec<-set_automodel(init_spec,
#                        enabled = FALSE,
#                        acceptdefault = TRUE)