describe |

Computes and displays descriptive statistics for the pooled data.

Syntax

pool_name.describe(options) pool_ser1 [pool_ser2 pool_ser3 ...]

List the name of ordinary and pool series for which you wish to compute descriptive statistics.

By default, statistics are computed for each stacked pool series, using only common observations where all of the cross-sections for a given series have nonmissing data. A missing observation for a series in any one cross-section causes that observation to be dropped for all cross-sections for the corresponding series. You may change the default treatment of NAs using the “i” and “b” options.

EViews also allows you to compute statistics with the cross-section means removed, statistics for each cross-sectional series in a pool series, and statistics for each period, taken across all cross-section units.

Options

m | Stack data and subtract cross-section specific means from each variable—this option provides the within estimators. |

c | Do not stack data—compute statistics individually for each cross-sectional unit. |

t | Time period specific—compute statistics for each period, taken over all cross-section identifiers. |

i | Individual sample—includes every valid observation for the series even if data are missing from other series in the list. |

b | Balanced sample—constrains each cross-section to have the same observations. If an observation is missing for any series, in any cross-section, it will be dropped for all cross-sections. |

prompt | If no pool series are specified, force the dialog to appear from within a program. |

p | Print the descriptive statistics. |

Examples

pool1.describe(m) gdp? inv? cpi?

displays the “within” descriptive statistics of the three series GDP, INV, CPI for the POOL1 cross-section members.

pool1.describe(t) gdp?

computes the statistics for GDP for each period, taken across each of the cross-section identifiers.

Cross-references

See
“Pooled Time Series, Cross-Section Data” for a discussion of the computation of these statistics, and a description of individual and balanced samples.