Function allowing to customize the ARIMA model structure when the automatic modelling is disabled.(see example)
Arguments
- x
the specification to customize, must be a "SPEC" class object (see details).
- mean
to fix the coefficient of the mean. If
mean = 0
, the mean is disabled.- mean.type
a character defining the mean coefficient estimation procedure. Possible procedures are:
"Undefined"
= no use of any user-defined input (i.e. coefficient is estimated),"Fixed"
= the coefficients are fixed at the value provided by the user,"Initial"
= the value defined by the user is used as the initial condition.- p, d, q, bp, bd, bq
to specify the order of the SARIMA model in the form ARIMA(p,d,q)(bp,bd,bd).
- coef
a vector providing the coefficients for the regular and seasonal AR and MA polynomials. The vector length must be equal to the sum of the regular and seasonal AR and MA orders. The coefficients shall be provided in the following order: regular AR (Phi;
p
elements), regular MA (Theta;q
elements), seasonal AR (BPhi;bp
elements) and seasonal MA (BTheta;bq
elements). E.g.:arima.coef=c(0.6,0.7)
withp=1, q=0,bp=1
andbq=0
.- coef.type
a vector defining the ARMA coefficients estimation procedure. Possible procedures are:
"Undefined"
= no use of any user-defined input (i.e. coefficients are estimated),"Fixed"
= the coefficients are fixed at the value provided by the user,"Initial"
= the value defined by the user is used as the initial condition.
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/
Examples
# create default spec
# my_spec<-rjd3x13::x13_spec("rsa5c")
# disable automatic arima modelling
# my_spec<-set_automodel(my_spec, enabled = FALSE)
# customize arima model
# my_spec <-set_arima(my_spec,mean = 0.2,
# mean.type = "Fixed",
# p = 1, d = 2, q = 0,
# bp = 1, bd = 1, bq = 0,
# coef = c(0.6,0.7),
# coef.type = c("Initial","Fixed"))