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Produce exact maximum likelihood estimates of the parameters of a scalar ARMA model.
xsarma(y, arcoefi, macoefi)
initial gradient.
initial (-2)log likelihood.
final estimates of AR coefficients.
final estimates of MA coefficients.
final gradient.
final ALPH (AR part) at subroutine ARCHCK.
final ALPH (MA part) at subroutine ARCHCK.
final (-2)log likelihood.
white noise variance.
a univariate time series.
initial estimates of AR coefficients.
initial estimates of MA coefficients.
The ARMA model is given by
H.Akaike (1978) Covariance matrix computation of the state variable of a stationary Gaussian process. Research Memo. No.139. The Institute of Statistical Mathematics.
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
# "arima.sim" is a function in "stats".
# Note that the sign of MA coefficient is opposite from that in "timsac".
arcoef <- c(1.45, -0.9)
macoef <- c(-0.5)
y <- arima.sim(list(order=c(2,0,1), ar=arcoef, ma=macoef), n = 100)
arcoefi <- c(1.5, -0.8)
macoefi <- c(0.0)
z <- xsarma(y, arcoefi, macoefi)
z$arcoef
z$macoef
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