armafit {timsac}

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), n = 1000)
z <- armafit(y, model.order = c(2,1))
z$arcoef
z$macoef