exsar {timsac} | R Documentation |
Produce exact maximum likelihood estimates of the parameters of a scalar AR model.
exsar(y, max.order=NULL, plot=FALSE, tmp.file=NULL)
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. |
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.
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. |
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
data(Canadianlynx) z <- exsar(Canadianlynx, max.order=14) z$arcoef.maice z$arcoef.mle