tseries: arma – R documentation

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arma

Fit ARMA Models to Time Series

Description

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

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 from `lag` is used.
`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] + … + a[p]y[t-p] + b[1]e[t-1] + … + 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:

`lag`	the lag specification of the fitted model.
`coef`	estimated ARMA coefficients for the fitted model.
`css`	the conditional sum-of-squared errors.
`n.used`	the number of observations of `x`.
`residuals`	the series of residuals.
`fitted.values`	the fitted series.
`series`	the name of the series `x`.
`frequency`	the frequency of the series `x`.
`call`	the call of the `arma` function.
`vcov`	estimate of the asymptotic-theory covariance matrix for the coefficient estimates.
`convergence`	The `convergence` integer code from `optim`.
`include.intercept`	Does the model contain an intercept?

Author(s)

A. Trapletti

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)

tseries

Time Series Analysis and Computational Finance

v0.10-48

GPL-2

Authors

Adrian Trapletti [aut], Kurt Hornik [aut, cre], Blake LeBaron [ctb] (BDS test code)

Initial release