ar.burg(x, aic=T, order.max=<<see below>>)
TRUE
, use the Akaike information criterion to choose the
best order not greater than
order.max
.
If
FALSE
,
order.max
will be the order of the fitted model.
10 * log10(n.used/ncol(x))
.
aic=TRUE
, then this is the order less than or equal to
order.max
which minimizes the AIC, otherwise, it is
order.max
.
order
by "nser"
by "nser" array, where "nser" is the number of univariate components
of
x
(
1
or
ncol(x)
).
If
order
is
0
,
ar
will have dimensions
1
by "nser" by "nser"
and will be filled with zeros.
The first level of the first dimension corresponds to one observation back in
time, the second level corresponds to two observations back, etc.
The second dimension corresponds to the predicted series, and the third
corresponds to the predicting series.
ar
.
0
through
order.max
.
These have the minimum value subtracted from all of them so the minimum
is always zero.
order.max
.
ar
are used in the forward direction on the
series with mean(s) removed.
"burg"
.
x
.
The a(i) coefficients i=1,...,
order.max
are estimated by minimizing
a sum of backward and forward prediction squared errors
(Burg's algorithm).
The output may be used in
spec.ar
to estimate the spectrum of the process.
The estimation is performed using the sample mean of each univariate
series as the estimate of the mean.
Remember that the coefficients in
ar
are for the series with the mean(s)
removed.
Burg, J. P. (1967). Maximum Entropy Spectral Analysis. Paper presented at the 37th Annual International S.E.G. Meeting, Oklahoma City, Oklahoma.
Estimation for multivariate series appears to be incorrect (it gives slightly different answers when the series are permuted).
a <- ar.burg(log(lynx)) tsplot(a$aic) # Fit an AR(11) to this time series llynx.ar.fit <- ar.burg(log(lynx), aic=F, order=11)