code: def estimator_cov(self,method):
                   
          
          Y = np.array([reg[self.lags:] for reg in self.data])    
          Z = self._create_Z(Y)
          if method == 'OLS':
              sigma = self.ols_covariance()
          else:           
              sigma = self.custom_covariance(self.latent_variables.get_z_values())
          return np.kron(np.linalg.inv(np.dot(Z,np.transpose(Z))), sigma)
docs: Creates covariance matrix for the estimators
  
          Parameters
          ----------
          method : str
              Estimation method
  
          Returns
          ----------
          A Covariance Matrix