Basic time series statistics
statsbasicStats.Rd
Computes basic financial time series statistics.
Arguments
 x

an object of class
"timeSeries"
or any other object which can be transformed by the functionas.timeSeries
into an object of class"timeSeries"
. The latter case, other than"timeSeries"
objects, is more or less untested.  ci
confidence interval, a numeric value, by default 0.95, i.e. 95%.
Details
Computes a number of sample statistics for each column of
x
. The statistics should be clear from the row names of the
returned data frame.
"LCL"
and "UCL"
stand for lower/upper confidence limits,
computed under the null hypothesis of i.i.d.
"Kurtosis"
represents the 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 x
and the
following rows:
 "nobs"
number of observations,
 "NAs"
number of
NA
s "Minimum"
minimum,
 "Maximum "
maximum,
 "1. Quartile"
lower quartile,
 "3. Quartile"
upper quartile,
 "Mean"
mean,
 "Median"
median,
 "Sum"
sum of the values,
 "SE Mean"
standard error of the mean,
 "LCL Mean"
lower limit of the CI for the mean,
 "UCL Mean"
upper limit of the CI for the mean,
 "Variance"
variance,
 "Stdev"
standard deviation,
 "Skewness"
skewness coefficient,
 "Kurtosis"
excess kurtosis.
Examples
set.seed(1234)
## basicStats 
# Simulated Monthly Return Data:
tS = timeSeries(matrix(rnorm(12)), timeDate::timeCalendar())
basicStats(tS)
#> TS.1
#> nobs 12.000000
#> NAs 0.000000
#> Minimum 2.345698
#> Maximum 1.084441
#> 1. Quartile 0.917125
#> 3. Quartile 0.315353
#> Mean 0.442263
#> Median 0.555542
#> Sum 5.307153
#> SE Mean 0.265001
#> LCL Mean 1.025526
#> UCL Mean 0.141000
#> Variance 0.842706
#> Stdev 0.917990
#> Skewness 0.233217
#> Kurtosis 0.571935