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Perform FPE(Final Prediction Error) computation for one-dimensional AR model.
fpeaut(y, max.order = NULL)
order of minimum FPE.
AR coefficients with minimum FPE.
= sigma2(ordermin).
sigma2
ordermin
minimum FPE.
minimum RFPE.
OFPE.
AR coefficients.
\(\sigma^2.\)
FPE (Final Prediction Error).
RFPE.
partial correlation.
chi-squared.
a univariate time series.
upper limit of model order. Default is \(2 \sqrt{n}\), where \(n\) is the length of the time series y.
y
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.
H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control of Dynamic Systems. Kluwer Academic publishers.
y <- arima.sim(list(order=c(2,0,0), ar=c(0.64,-0.8)), n = 200) fpeaut(y, max.order = 20)
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