Creates the structure of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        Returns
        ----------
        sigma2 : np.array
            Contains the values for the conditional volatility series

        Y : np.array
            Contains the length-adjusted time series (accounting for lags)

        eps : np.array
            Contains the squared residuals (ARCH terms) for the time series

def _model(self, beta):
        """ Creates the structure of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        Returns
        ----------
        sigma2 : np.array
            Contains the values for the conditional volatility series

        Y : np.array
            Contains the length-adjusted time series (accounting for lags)

        eps : np.array
            Contains the squared residuals (ARCH terms) for the time series
        """

        # Transform latent variables
        parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])

        xeps = np.power(self.data-parm[-1],2)
        Y = np.array(self.data[self.max_lag:])
        eps = np.power(Y-parm[-1],2)
        X = np.ones(Y.shape[0])

        # ARCH terms
        if self.q != 0:
            for i in range(0,self.q):   
                X = np.vstack((X,xeps[(self.max_lag-i-1):-i-1]))
            sigma2 = np.matmul(np.transpose(X),parm[0:-self.p-1])
        else:
            sigma2 = np.transpose(X*parm[0])

        sigma2 = garch_recursion(parm, sigma2, self.q, self.p, Y.shape[0], self.max_lag)

        return np.array(sigma2), Y, eps