This function collapses the polynomials of an ARIMA model
into two polynomials: the product of the autoregressive polynomials
and the product of the moving average polynomials.
Usage
coefs2poly(x, morder, add = TRUE, ...)
Arguments
x
a numeric vector containing the coefficients from the
fitted ARIMA model or an Arima object returned by arima.
morder
a numeric vector. The order of the fitted model as returned
by arima in element arma. Ignored if x is an
Arima object.
add
logical. If TRUE, the polynomial of the differencing filter
(if present in the model) is multiplied byt the stationary autoregressive
polynomial. Otherwise only the coefficients of the product of the
stationary polynomials is returned.
...
Further arguments to be passed to other functions. Currently ignored.
Details
In practice, the version coefs2poly.Arima may be more convenient since it
requires passing only one argument defining the model. However, since only the
coefficients of the model and the order of the model is required by this function,
there is no need to pass the complete Arima object.
Value
A list containing the elements:
arcoefs, the coefficients of the product of the
autoregressive polynomials;
macoefs, the coefficients of the product of the
moving average polynomials.
This list is of class "ArimaPars" so that it can be recognized by
outliers.tstatistics.
Note
If the ARIMA model contains regressor variables,
the names of those variables cannot match the following
regular expressions:
“^ard+$”, “^sard+$”, “^mad+$”,
and “^smad+$”. Otherwise the AR and MA coefficients
would be misled by the coefficients of the external regressors.
See Also
polynomial,
Ops.polynomial.
Examples
# ARIMA(0,1,1)(0,1,1) model
fit <- arima(log(AirPassengers), order = c(0,1,1),
seasonal = list(order = c(0,1,1)))
coefs <- coef(fit)
# "coefs2poly" returns the coefficients of the product of
# the non-seasonal and the seasonal moving average polynomials
pma <- polynom::polynomial(c(1, coefs[1]))
psma <- polynom::polynomial(c(1, rep(0, 11), coefs[2]))
coef(pma * psma)[-1]
coefs2poly(coef(fit), fit$arma)$macoefs
# since the model does not contain an autoregressive part
# the product of the regular and the seasonal differencing
# filter is returned if "add = TRUE"
coefs2poly(coef(fit), fit$arma)$arcoefs
# an empty set nothing is returned if "add = FALSE"
coefs2poly(coef(fit), fit$arma, add = FALSE)$arcoefs
# in a model with non-seasonal part and no differencing filter
# no multiplication of polynomials are involved and
# the output coincides with "coef"
fit <- arima(log(AirPassengers), order = c(1,0,1))
coef(fit)
coefs2poly(coef(fit), fit$arma)
providing 
.