R: ARMA Model Fitting

armafit {timsac} | R Documentation

## ARMA Model Fitting

### Description

Fit an ARMA model with specified order by using DAVIDON's algorithm.

### Usage

      armafit(y, model.order)

### Arguments

`y` | a univariate time series.
`model.order` | a numerical vector of the form c(ar, ma) which gives the order to be fitted successively.

### Details

The maximum likelihood estimates of the coefficients of a scalar ARMA model

y(t) - a(1)y(t-1) -...- a(p)y(t-p) = u(t) - b(1)u(t-1) -...- b(q)u(t-q)

of a time series y(t) are obtained by using DAVIDON's algorithm. Pure autoregression is not allowed.

### Value

`arcoef` | maximum likelihood estimates of AR coefficients.
`macoef` | maximum likelihood estimates of MA coefficients.
`arstd` | standard deviation (AR).
`mastd` | standard deviation (MA).
`v` | innovation variance.
`aic` | AIC.
`grad` | final gradient.

### References

H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.5, Timsac74, A Time Series Analysis and Control Program Package (1). The Institute of Statistical Mathematics.

### Examples

      # "arima.sim" is a function in "stats".
      # Note that the sign of MA coefficient is opposite from that in "timsac".
      y <- arima.sim(list(order=c(2,0,1), ar = c(0.64,-0.8), ma=-0.5), 1000)
      z <- armafit(y, model.order=c(2,1))
      z$arcoef
      z$macoef

[Package timsac version 1.3.5 Index]