timsac (version 1.3.0)

fpeaut: FPE Auto

Description

Perform FPE(Final Prediction Error) computation for one-dimensional AR model.

Usage

fpeaut(y, max.order=NULL)

Arguments

y

a univariate time series.

max.order

upper limit of model order. Default is $2 \sqrt{n}$, where $n$ is the length of the time series y.

Value

• ordermin
  order of minimum FPE.
• best.ar
  AR coefficients with minimum FPE.
• sigma2m
  = sigma2(ordermin).
• fpemin
  minimum FPE.
• rfpemin
  minimum RFPE.
• ofpe
  OFPE.
• arcoef
  AR coefficients.
• sigma2
  $\sigma^2.$
• fpe
  FPE (Final Prediction Error).
• rfpe
  RFPE.
• parcor
  partial correlation.
• chi2
  chi-squared.

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.

References

H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control of Dynamic Systems. Kluwer Academic publishers.

Examples

y <- arima.sim(list(order=c(2,0,0), ar=c(0.64,-0.8)), n=200)
  fpeaut(y, max.order=20)