TSA (version 1.0)

arima: Fitting an ARIMA model with Exogeneous Variables

Description

This function is identical to the arimax function which builds on and extends the capability of the arima function in R stats by allowing the incorporation of transfer functions, and 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. This function is heavily based on the arima function of the stats core of R.

Usage

arima(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 models.

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 in 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, assum

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})

transfer

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

Value

• An Arimax object contining the model fit.

See Also

arima

Examples

data(hare)
arima(sqrt(hare),order=c(3,0,0))