utils.utils.get_ssc

utils.utils.get_ssc(
    ssc_dict,
    N,
    k,
    k_fe,
    k_fe_nested,
    n_fe,
    n_fe_fully_nested,
    G,
    vcov_sign,
    vcov_type,
)

Compute small sample adjustment factors.

Parameters

Name	Type	Description	Default
ssc_dict	dict	A dictionary created via the ssc() function.	required
N	int	The number of observations.	required
k	int	The number of estimated parameters (as in the first part of the model formula)	required
k_fe	int	The number of estimated fixed effects (as specified in the second part of the model formula).	required
k_fe_nested	int	The number of estimated fixed effects nested within clusters.	required
n_fe	int	The number of fixed effects in the model. I.e. ‘Y ~ X1 | f1 + f2’ has 2 fixed effects.	required
n_fe_fully_nested	int	The number of fixed effects that are fully nested within clusters.	required
G	int	The number of clusters.	required
vcov_sign	array - like	A vector that helps create the covariance matrix.	required
vcov_type	str	The type of covariance matrix. Must be one of “iid”, “hetero”, “HAC”, or “CRV”.	required

Returns

Name	Type	Description
	tuple of np.ndarray and int	A small sample adjustment factor and the effective number of coefficients k used in the adjustment.

Raises

Name	Type	Description
	ValueError	If vcov_type is not “iid”, “hetero”, or “CRV”, or if G_df is neither “conventional” nor “min”.