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\name{adf.test}
\alias{adf.test}
\title{Augmented Dickey--Fuller Test}
\description{
Computes the Augmented Dickey-Fuller test for the null that \code{x} has
a unit root.
}
\usage{
adf.test(x, alternative = c("stationary", "explosive"),
k = trunc((length(x)-1)^(1/3)))
}
\arguments{
\item{x}{a numeric vector or time series.}
\item{alternative}{indicates the alternative hypothesis and must be
one of \code{"stationary"} (default) or \code{"explosive"}. You can
specify just the initial letter.}
\item{k}{the lag order to calculate the test statistic.}
}
\details{
The general regression equation which incorporates a constant and a
linear trend is used and the t-statistic for a first order
autoregressive coefficient equals one is computed. The number of lags
used in the regression is \code{k}. The default value of
\code{trunc((length(x)-1)^(1/3))} corresponds to the suggested upper
bound on the rate at which the number of lags, \code{k}, should be
made to grow with the sample size for the general \code{ARMA(p,q)}
setup. Note that for \code{k} equals zero the standard Dickey-Fuller
test is computed. The p-values are interpolated from Table 4.2, p. 103
of \bibcitet{R:Banerjee+Dolado+Galbraith:1993}.
If the computed statistic is outside the
table of critical values, then a warning message is generated.
Missing values are not allowed.
}
\value{
A list with class \code{"htest"} containing the following components:
\item{statistic}{the value of the test statistic.}
\item{parameter}{the lag order.}
\item{p.value}{the p-value of the test.}
\item{method}{a character string indicating what type of test was
performed.}
\item{data.name}{a character string giving the name of the data.}
\item{alternative}{a character string describing the alternative
hypothesis.}
}
\references{
\bibshow{R:Banerjee+Dolado+Galbraith:1993, Said+Dickey:1984}
}
\author{A. Trapletti}
\seealso{
\code{\link{pp.test}}
}
\examples{
x <- rnorm(1000) # no unit-root
adf.test(x)
y <- diffinv(x) # contains a unit-root
adf.test(y)
}
\keyword{ts}
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