exsar: Exact Maximum Likelihood Method of Scalar AR Model Fitting

In timsac: Time Series Analysis and Control Package

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

timsac documentation built on Sept. 30, 2023, 5:06 p.m.