linearmodels.panel.utility.generate_panel_data¶

generate_panel_data(nentity=971, ntime=7, nexog=5, const=False, missing=0, other_effects=2, ncats=4, rng=None)[source]¶

Parameters

nentityint: The number of entities in the panel.
ntimeint: The number of time periods in the panel.
nexogint: The number of explanatory variables in the dataset.
constbool: Flag indicating that the model should include a constant.
missingfloat: The percentage of values that are missing. Should be between 0 and 100.
other_effectsint: The number of other effects generated.
ncatsUnion[int, Sequence[int]]: The number of categories to use in other_effects and variance clusters. If list-like, then it must have as many elements as other_effects.
rngRandomState: A NumPy RandomState instance. If not provided, one is initialized using a fixed seed.

Returns

PanelModelData

A namedtuple derived class containing 4 DataFrames:

data - A simulated data with variables y and x# for # in 0,…,4. If const is True, then also contains a column named const.
weights - Simulated non-negative weights.
other_effects - Simulated effects.
clusters - Simulated data to use in clustered covariance estimation.