timsac (version 1.3.0)

armafit: 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.

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.

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.

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