arima: Fitting an ARIMA model with Exogeneous Variables

In TSA: Time Series Analysis

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, see the help page of this function for details on arguments x to kappa.

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
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, and this is not done automatically.
transfer	a list consisting of the ARMA orders for each transfer (distributed lag) covariate.

Value

An Arimax object contining the model fit.

Author(s)

Original author of the arima function in R stats: Brian Ripley. The arimax function is based on the stats:::arima function, with modifications by Kung-Sik Chan.

See Also

arima

Examples

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

TSA documentation built on July 5, 2022, 5:05 p.m.