Create and save series of descriptive statistics computed from a pool object.
Syntax
pool_name.makestats(options) pool_series1 [pool_series2 ...] @ stat_list
You should provide options, a list of series names, an “@” separator, and a list of command names for the statistics you wish to compute. The series will have a name with the cross-section identifier “?” replaced by the statistic command.
Options
Options in parentheses specify the sample to use to compute the statistics
i | Use individual sample. |
c (default) | Use common sample. |
b | Use balanced sample. |
o | Force the overwrite of the computed statistics series if they already exist. The default creates a new series using the next available names. |
prompt | Force the dialog to appear from within a program. |
Command names for the statistics to be computed
obs | Number of observations. |
mean | Mean. |
med | Median. |
var | Variance. |
sd | Standard deviation. |
skew | Skewness. |
kurt | Kurtosis. |
jarq | Jarque-Bera test statistic. |
min | Minimum value. |
max | Maximum value. |
Examples
pool1.makestats gdp_? edu_? @ mean sd
computes the mean and standard deviation of the GDP_? and EDU_? series in each period (across the cross-section members) using the default common sample. The mean and standard deviation series will be named GDP_MEAN, EDU_MEAN, GDP_SD, and EDU_SD.
pool1.makestats(b) gdp_? @ max min
Computes the maximum and minimum values of the GDP_? series in each period using the balanced sample. The max and min series will be named GDP_MAX and GDP_MIN.
Cross-references
See
“Pooled Time Series, Cross-Section Data” for details on the computation of these statistics and a discussion of the use of individual, common, and balanced samples in pool.