Fe

Get underlying data

econometrics.fe(regression_variables: List[Tuple], data: Dict[str, pandas.core.frame.DataFrame], entity_effects: bool = False, time_effects: bool = False) -> Tuple[pandas.core.frame.DataFrame, Any, List[Any], Any]

When effects are correlated with the regressors the RE and BE estimators are not consistent. The usual solution is to use Fixed Effects which are called entity_effects when applied to entities and time_effects when applied to the time dimension. [Source: LinearModels]

Parameters
----------
regression_variables : list
    The regressions variables entered where the first variable is
    the dependent variable.
data : dict
    A dictionary containing the datasets.
entity_effects : bool
    Whether to include entity effects
time_effects : bool
    Whether to include time effects

Returns
-------
The dataset used, the dependent variable, the independent variable and
the OLS model.