arma

From tseries v0.10-10 by Kurt Hornik

Fit ARMA Models to Time Series

Fit an ARMA model to a univariate time series by conditional least squares. For exact maximum likelihood estimation see arima0.

Keywords: ts

Usage

arma(x, order = c(1, 1), lag = NULL, coef = NULL,
     include.intercept = TRUE, series = NULL, qr.tol = 1e-07, ...)

Arguments

x: a numeric vector or time series.
order: a two dimensional integer vector giving the orders of the model to fit. order[1] corresponds to the AR part and order[2] to the MA part.
lag: a list with components ar and ma. Each component is an integer vector, specifying the AR and MA lags that are included in the model. If both, order and lag, are given, only the specification
coef: If given this numeric vector is used as the initial estimate of the ARMA coefficients. The preliminary estimator suggested in Hannan and Rissanen (1982) is used for the default initialization.
include.intercept: Should the model contain an intercept?
series: name for the series. Defaults to deparse(substitute(x)).
qr.tol: the tol argument for qr when computing the asymptotic standard errors of coef.
...: additional arguments for optim when fitting the model.

Details

The following parametrization is used for the ARMA(p,q) model: $$y[t] = a[0] + a[1]y[t-1] + \dots + a[p]y[t-p] + b[1]e[t-1] + \dots + b[q]e[t-q] + e[t],$$ where $a[0]$ is set to zero if no intercept is included. By using the argument lag, it is possible to fit a parsimonious submodel by setting arbitrary $a[i]$ and $b[i]$ to zero. arma uses optim to minimize the conditional sum-of-squared errors. The gradient is computed, if it is needed, by a finite-difference approximation. Default initialization is done by fitting a pure high-order AR model (see ar.ols). The estimated residuals are then used for computing a least squares estimator of the full ARMA model. See Hannan and Rissanen (1982) for details.

Value

A list of class "arma" with the following elements:
lagthe lag specification of the fitted model.
coefestimated ARMA coefficients for the fitted model.
cssthe conditional sum-of-squared errors.
n.usedthe number of observations of x.
residualsthe series of residuals.
fitted.valuesthe fitted series.
seriesthe name of the series x.
frequencythe frequency of the series x.
callthe call of the arma function.
asy.se.coefthe asymptotic-theory standard errors of the coefficient estimates.
convergenceThe convergence integer code from optim.
include.interceptDoes the model contain an intercept?

References

E. J. Hannan and J. Rissanen (1982): Recursive Estimation of Mixed Autoregressive-Moving Average Order. Biometrika 69, 81--94.

Examples

data(tcm)  
r <- diff(tcm10y)
summary(r.arma <- arma(r, order = c(1, 0)))
summary(r.arma <- arma(r, order = c(2, 0)))
summary(r.arma <- arma(r, order = c(0, 1)))
summary(r.arma <- arma(r, order = c(0, 2)))
summary(r.arma <- arma(r, order = c(1, 1)))
plot(r.arma)

data(nino)
s <- nino3.4
summary(s.arma <- arma(s, order=c(20,0)))
summary(s.arma
         <- arma(s, lag=list(ar=c(1,3,7,10,12,13,16,17,19),ma=NULL)))
acf(residuals(s.arma), na.action=na.remove)
pacf(residuals(s.arma), na.action=na.remove)
summary(s.arma
         <- arma(s, lag=list(ar=c(1,3,7,10,12,13,16,17,19),ma=12)))
summary(s.arma
         <- arma(s, lag=list(ar=c(1,3,7,10,12,13,16,17),ma=12)))
plot(s.arma)

Documentation reproduced from package tseries, version 0.10-10, License: GPL-2

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