exsar {timsac}R Documentation

Exact Maximum Likelihood Method of Scalar AR Model Fitting

Description

Produce exact maximum likelihood estimates of the parameters of a scalar AR model.

Usage

  exsar(y, max.order=NULL, plot=FALSE, tmp.file=NULL)

Arguments

y a univariate time sries.
max.order upper limit of AR order. Default is 2*sqrt(n), where n is the length of the time series y.
plot logical. If TRUE daic is plotted.
tmp.file a character string naming a file written intermediate results of minimization by DAVIDON-FLETCHER-POWELL procedure. If NULL (default) output no file.

Details

The AR model is given by

y(t) = a(1)y(t-1) + .... + a(p)y(t-p) + u(t)

where p is AR order and u(t) is a zero mean white noise.

Value

mean mean.
var variance.
v innovation variance.
aic AIC.
aicmin minimum AIC.
daic AIC-aicmin.
order.maice order of minimum AIC.
v.maice MAICE innovation variance.
arcoef.maice MAICE AR coefficients.
v.mle maximum likelihood estimates of innovation variance.
arcoef.mle maximum likelihood estimates of AR coefficients.

References

H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.

Examples

  data(Canadianlynx)
  z <- exsar(Canadianlynx, max.order=14)
  z$arcoef.maice
  z$arcoef.mle

[Package timsac version 1.2.1 Index]