armafit {timsac}R Documentation

ARMA Model Fitting

Description

Fit an ARMA model with specified order by using DAVIDON's algorithm.

Usage

  armafit(y, model.order, tmp.file=NULL)

Arguments

y a univariate time series.
model.order a numerical vector of the form c(ar, ma) which gives the order to be fitted successively.
tmp.file a character string naming a file written intermediate results of model fitting. If NULL (default) output no file.

Details

The maximum likelihood estimates of the coefficients of a scalar ARMA model

y(t) - a(1)y(t-1) -...- a(p)y(t-p) = u(t) - b(1)u(t-1) -...- b(q)u(t-q)

of a time series y(t) are obtained by using DAVIDON's algorithm. Pure autoregression is not allowed.

Value

arcoef maximum likelihood estimates of AR coefficients.
macoef maximum likelihood estimates of MA coefficients.
arstd standard deviation (AR).
mastd standard deviation (MA).
v innovation variance.
aic AIC.
grad final gradient.

References

H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.5, Timsac74, A Time Series Analysis and Control Program Package (1). The Institute of Statistical Mathematics.

Examples

  # "arima.sim" is a function in "stats".
  # Note that the sign of MA coefficient is opposite from that in "timsac".
  y <- arima.sim(list(order=c(2,0,1), ar = c(0.64,-0.8), ma=c(-0.5)), n=1000)
  z <- armafit(y, model.order=c(2,1))
  z$arcoef
  z$macoef

[Package timsac version 1.2.1 Index]