TWFE

class differences.twfe.twfe.TWFE(data: DataFrame, cohort_name: str, cohort_data: DataFrame | None = None, intensity_name: str | None = None, freq: str | None = None, stacked: bool = False)

Two-way fixed effect regression

• balanced panels, unbalanced panels

• two or multiple periods

• fixed or staggered treatment timing

• binary treatment, with various intensities

• one or multiple treatments per entity

Parameters:

• data (DataFrame) –

  pandas DataFrame

  df = df.set_index(['entity', 'time'])
  

  where df is the dataframe to use, ‘entity’ should be replaced with the name of the entity column and ‘time’ should be replaced with the name of the time column.

• cohort_name (str) – cohort name

• cohort_data (DataFrame) – cohort data, in place of cohort name

• intensity_name (str) – name of the column with treatment intensities

• freq (str | None, default: None) – the date frequency of the panel data. Required if the time index is datetime. For example, if the time column is a monthly datetime then freq=’M’. Check offset aliases, for a list of available frequencies.

fit(formula: str, start: int = None, end: int = None, reference_period: int | None = -1, bin_endpoints: bool | str = True, weights_name: str = None, cluster_names: str | list = None, alpha: float = 0.05, use_intensity: bool = False, drop_singletons: bool = True, drop_absorbed: bool = False, dummies_names: list[str] = None, drop_names: list[str] = None)

fit two-way fixed effect

Parameters:

• formula (str) –

  Wilkinson formula for the outcome variable and covariates

  If no covariates the formula must contain only the name of the outcome variable

  # example with covariates
  formula = 'y ~ a + b + a:b'
  
  # example without covariates
  formula = 'y'
  

  Formulas are implemented using formulaic, refer to its documentation for additional details.

• start – first relative period

• end – last relative period

• reference_period – reference period

• bin_endpoints –

  weights_name: str | None, default: None

  The name of the column containing the sampling weights. If None, all observations have same weights.

• weights_name –

• cluster_names –

• alpha –

• use_intensity –

• drop_singletons –

• drop_absorbed –

• dummies_names –

• drop_names –