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${project.parent.artifactId} - ${project.artifactId}
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 LjungBox limit,
the model is rejected, the outlier critical value is reduced, and model and outlier identification (if specified) is redone with a reduced value.
The threshold value for t-statistics of ARMA coefficients and constant term used for the final test of model parsimony.
If the highest order ARMA coefficient has a t-value less than this value in magnitude, the order of the model is reduced.
Also if the constant term has a t-value less than ArmaLimit in magnitude, it is removed from the set of regressors.
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.
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 Cancelation limit)). If there is no cancellation, the AR root is set equal to unity (i.e. the differencing order changes).
The threshold value for the final unit root test.
If the magnitude of an AR root for the final model is less than this number,
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.
If the difference in moduli of an AR and an MA roots (when estimating ARIMA(1,0,1)(1,0,1) models in the second step of the automatic identification of the differencing orders)
is smaller than Cancelation limit, the two roots are assumed equal and cancel out.
The percentage by which the outlier’s 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)×CV, where CV is the original critical value.
Controls whether ARIMA models with nonseasonal AR and MA terms or seasonal AR and MA terms
will be considered in the automatic model identification procedure.
When this option is disabled, a model with AR and MA terms in both the seasonal and nonseasonal parts of the model can be acceptable,
provided there are not AR and MA terms in either the seasonal or non-seasonal.
Controls whether 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 operator is equal to the order of the combined MA operator).
Option for an automatic correction for the leap year effect (see Adjust).
It is used only in case of automatic transformation.
It is used to set the critical value for outlier
detection. If defined, the critical values of the single outlier specification
are unused.
Tramo code: DELTATC. It affects only the TC outlier, which is equal to an
impulse divided by (1 - tcrate B).
Determines how the program successively adds detected outliers to the model.
At present only the AddOne method is supported.
When marked, the program computes a seasonal component.
Otherwise, the seasonal component is not estimated and its values are all
set to 0 (additive decomposition) or 1 (multiplicative decomposition).
Lower bound of SIGMA parameter.
Default value is 1.5.
Upper bound of SIGMA parameter. Should be higher than lsigma.
Default value is 2.5.
Specifies which seasonal moving average (i.e. seasonal filter) will be used to estimate the seasonal factors for the entire series.
The program can choose the final seasonal filter automatically (Msr).
The same filter can be used for all the periods (Filter) or different filters can be specified for each period (Filters)
Length of the Henderson filter used for trend.
Should be 0 or any odd number between 1 and 99.
If 0, the length is automatically identified by the software.
Specifies if the standard errors used for extreme values detection and adjustment are computed separately for each calendar month/quarter(All)
or only if Cochran’s hypothesis test determines that the irregular component is heteroskedastic by calendar month/quarter (signif)
Specifies that the standard errors used for extreme values detection are computed separately for two complementary sets of calendar months/quarters
The vector specifies the group of each period (1 or 2)
If excludeforecasts=yes, forecasts and backcasts from the regARIMA model are not used in the generation of extreme values
in the seasonal adjustment routines. The default is excludefcst=no,
which allows the full forecast and backcast extended series to be used in the extreme value process.
Information on the way a parameter should be used or interpreted.
A fixed parameter is an "a priori" value, which doesn't result from an estimation procedure.
An initial parameter is a parameter that should be used as a starting value in estimation procedures.
An estimated parameter is the results of an estimation procedure.
A derived parameter has been computed from other (usually estimated)
parameters.
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