def hqic(llf, nobs, df_modelwc):
 return (((-2.0) * llf) + ((2 * np.log(np.log(nobs))) * df_modelwc))

def hqic ( llf , nobs , df_modelwc ) : return ( ( ( - 2.0 ) * llf ) + ( ( 2 * np . log ( np . log ( nobs ) ) ) * df_modelwc ) )
hannan-quinn information criterion parameters llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant returns hqic : float information criterion references wikipedia doesnt say much .