code: def _model(self, data, beta):
               
  
          T, Z, R, Q, H = self._ss_matrices(beta)
  
          return univariate_kalman(data,Z,H,T,Q,R,0.0)
docs: Creates the structure of the model
  
          Parameters
          ----------
          data : np.array
              Contains the time series
  
          beta : np.array
              Contains untransformed starting values for latent variables
  
          Returns
          ----------
          a,P,K,F,v : np.array
              Filted states, filtered variances, Kalman gains, F matrix, residuals