R: Exact Maximum Likelihood Method of Scalar AR Model Fitting

exsar {timsac} | R Documentation

## 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

[Package timsac version 1.3.6 Index]