TSA (version 1.3)

arimax: Fitting an ARIMA model with Exogeneous Variables

Description

This function builds on and extends the capability of the arima function in R stats by allowing the incorporation of transfer functions, innovative and additive outliers. For backward compatitibility, the function is also named arima. Note in the computation of AIC, the number of parameters excludes the noise variance.

Usage

arimax(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0, 0), period = NA),
 xreg = NULL, include.mean = TRUE, transform.pars = TRUE, fixed = NULL, 
init = NULL, method = c("CSS-ML", "ML", "CSS"), n.cond, optim.control = list(),
 kappa = 1e+06, io = NULL, xtransf, transfer = NULL)

Arguments

x

time series response

order

regular ARIMA order

seasonal

seasonal ARIMA order

xreg

a dataframe containing covariates

include.mean

if true, an intercept term is incorporated in the model; applicable only to stationary model.

transform.pars

if true, the AR parameters are transformed to ensure stationarity

fixed

a vector indicating which coefficients are fixed or free

init

initial values

method

estimation method

n.cond

number of initial values to be conditioned on a conditional analysis

optim.control

control parameters for the optimization procedure

kappa

prior variance; used in dealing with initial values

All of the above parameters have the same usage as those in the arima function. Please check the help manual of the arima function. Below are new options.

io

a list of time points at which the model may have an innovative outlier. The time point of the outlier can be given either as absolute time point or as c(a,b), i.e. at the b-th 'month' of the a-th 'year' where each year has frequency(x) months, assuming x is a time series.

xtransf

xtranf is a matrix with each column containing a covariate that affects the time series response in terms of an ARMA filter of order (p,q), i.e. if Z is one such covariate, its effect on the time series is \((theta_0+theta_1B+...+theta_{q-1}B^{q-1})/(1-phi_1 B -...-phi_p B^p) Z_t\) In particular, if \(p=0\) and \(q=1\), this specifies a simple regression relationship, which should be included in xreg and not here. Note that the filter starts with zero initial values. Hence, it is pertinent to mean-delete each distributed-lag covariate, which is not done automatically.

transfer

a list consisting of the ARMA orders for each transfer (distributed lag) covariate.

Value

An Arimax object containing the model fit.

See Also

arima

Examples

# NOT RUN {
data(airmiles)
plot(log(airmiles),ylab='Log(airmiles)',xlab='Year', main='')
acf(diff(diff(window(log(airmiles),end=c(2001,8)),12)),lag.max=48,main='')
air.m1=arimax(log(airmiles),order=c(0,1,1),seasonal=list(order=c(0,1,1),
period=12),xtransf=data.frame(I911=1*(seq(airmiles)==69),
I911=1*(seq(airmiles)==69)),
transfer=list(c(0,0),c(1,0)),xreg=data.frame(Dec96=1*(seq(airmiles)==12),
Jan97=1*(seq(airmiles)==13),Dec02=1*(seq(airmiles)==84)),method='ML')


# }