linearmodels.panel.utility.generate_panel_data¶
-
linearmodels.panel.utility.generate_panel_data(nentity: int =
971, ntime: int =7, nexog: int =5, const: bool =False, missing: float =0, other_effects: int =2, ncats: int | list[int] =4, rng: RandomState | None =None) PanelModelData[source]¶ Simulate panel data for testing
- Parameters:¶
- nentity: int =
971¶ The number of entities in the panel.
- ntime: int =
7¶ The number of time periods in the panel.
- nexog: int =
5¶ The number of explanatory variables in the dataset.
- const: bool =
False¶ Flag indicating that the model should include a constant.
- missing: float =
0¶ The percentage of values that are missing. Should be between 0 and 100.
- other_effects: int =
2¶ The number of other effects generated.
- ncats: int | list[int] =
4¶ 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.
- rng: RandomState | None =
None¶ A NumPy RandomState instance. If not provided, one is initialized using a fixed seed.
- nentity: int =
- Returns:¶
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.
- Return type:¶