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\name{BasicStatistics}
\alias{basicStats}
\title{Basic time series statistics}
\description{
Computes basic financial time series statistics.
}
\usage{
basicStats(x, ci = 0.95)
}
\arguments{
\item{x}{
an object of class \code{"timeSeries"} or any other object which can
be transformed by the function \code{as.timeSeries} into an object
of class \code{"timeSeries"}. The latter case, other than
\code{"timeSeries"} objects, is more or less untested.
}
\item{ci}{
confidence interval, a numeric value, by default 0.95, i.e. 95\%.
}
}
\details{
Computes a number of sample statistics for each column of
\code{x}. The statistics should be clear from the row names of the
returned data frame.
\code{"LCL"} and \code{"UCL"} stand for lower/upper confidence limits,
computed under the null hypothesis of i.i.d.
\code{"Kurtosis"} represents the \emph{excess kurtosis}, so its
theoretical value for the normal distribution is zero, not 3.
These statistics are often computed as a first step in the study of
returns on financial assets. In that case any inference on these
statistics (including the confidence intervals for the mean) should be
considered exploratory, since returns are virtually never i.i.d.
}
\value{
a data frame with one column for each column of \code{x} and the
following rows:
\item{"nobs"}{number of observations,}
\item{"NAs"}{number of \code{NA}s}
\item{"Minimum"}{minimum,}
\item{"Maximum "}{maximum,}
\item{"1. Quartile"}{lower quartile,}
\item{"3. Quartile"}{upper quartile,}
\item{"Mean"}{mean, }
\item{"Median"}{median,}
\item{"Sum"}{sum of the values,}
\item{"SE Mean"}{standard error of the mean,}
\item{"LCL Mean"}{lower limit of the CI for the mean,}
\item{"UCL Mean"}{upper limit of the CI for the mean,}
\item{"Variance"}{variance, }
\item{"Stdev"}{standard deviation,}
\item{"Skewness"}{skewness coefficient,}
\item{"Kurtosis"}{excess kurtosis.}
}
\examples{
\dontshow{set.seed(1234)}
## Simulated Monthly Return Data
tS <- timeSeries(matrix(rnorm(12)), timeDate::timeCalendar())
basicStats(tS)
}
\keyword{stats}
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