exsar {timsac}

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)

Arguments

y

a univariate time series.

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.

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