Makes forecast with the estimated model

        Parameters
        ----------
        h : int (default : 5)
            How many steps ahead would you like to forecast?

        intervals : boolean (default: False)
            Whether to return prediction intervals

        oos_data : pd.DataFrame
            Data for the variables to be used out of sample (ys can be NaNs)

        Returns
        ----------
        - pd.DataFrame with predictions

def predict(self, h=5, intervals=False, oos_data=None, **kwargs):        
        """ Makes forecast with the estimated model

        Parameters
        ----------
        h : int (default : 5)
            How many steps ahead would you like to forecast?

        intervals : boolean (default: False)
            Whether to return prediction intervals

        oos_data : pd.DataFrame
            Data for the variables to be used out of sample (ys can be NaNs)

        Returns
        ----------
        - pd.DataFrame with predictions
        """     

        nsims = kwargs.get('nsims', 200)

        if self.latent_variables.estimated is False:
            raise Exception("No latent variables estimated!")
        else:

            _, X_oos = dmatrices(self.formula, oos_data)
            X_oos = np.array([X_oos])[0]
            full_X = self.X.copy()
            full_X = np.append(full_X,X_oos,axis=0)
            Z = full_X
            date_index = self.shift_dates(h)

            # Retrieve data, dates and (transformed) latent variables   
            if self.latent_variables.estimation_method in ['M-H']:
                lower_1_final = 0
                upper_99_final = 0
                lower_5_final = 0
                upper_95_final = 0
                forecasted_values_final = 0

                for i in range(nsims):
                    t_params = self.draw_latent_variables(nsims=1).T[0]
                    a, P = self._forecast_model(t_params, Z, h)

                    smoothed_series = np.zeros(h)
                    series_variance = np.zeros(h)

                    for t in range(h):
                        smoothed_series[t] = np.dot(Z[self.y.shape[0]+t],a[:,self.y.shape[0]+t])
                        series_variance[t] = np.dot(np.dot(Z[self.y.shape[0]+t],P[:,:,self.y.shape[0]+t]),Z[self.y.shape[0]+t].T)

                    forecasted_values = smoothed_series
                    lower_5 = smoothed_series - 1.96*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(t_params[0]),0.5)
                    upper_95 = smoothed_series + 1.96*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(t_params[0]),0.5)
                    lower_5_final += lower_5
                    upper_95_final += upper_95
                    lower_1 = smoothed_series - 2.575*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(t_params[0]),0.5)
                    upper_99 = smoothed_series + 2.575*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(t_params[0]),0.5)
                    lower_1_final += lower_1
                    upper_99_final += upper_99
                    forecasted_values_final += forecasted_values

                forecasted_values_final = forecasted_values_final / nsims
                lower_1_final = lower_1_final / nsims
                lower_5_final = lower_5_final / nsims
                upper_95_final = upper_95_final / nsims
                upper_99_final = upper_99_final / nsims

                if intervals is False:
                    result = pd.DataFrame(forecasted_values_final)
                    result.rename(columns={0:self.data_name}, inplace=True)
                else:
                    prediction_05 = lower_5_final
                    prediction_95 = upper_95_final
                    prediction_01 = lower_1_final
                    prediction_99 = upper_99_final

                    result = pd.DataFrame([forecasted_values_final, prediction_01, prediction_05, 
                        prediction_95, prediction_99]).T
                    result.rename(columns={0:self.data_name, 1: "1% Prediction Interval", 
                        2: "5% Prediction Interval", 3: "95% Prediction Interval", 4: "99% Prediction Interval"}, 
                        inplace=True)

                result.index = date_index[-h:]

                return result
     
            else:
                t_params = self.latent_variables.get_z_values()
                a, P = self._forecast_model(t_params, Z, h)
                smoothed_series = np.zeros(h)

                for t in range(h):
                    smoothed_series[t] = np.dot(Z[self.y.shape[0]+t],a[:,self.y.shape[0]+t])

                # Retrieve data, dates and (transformed) latent variables         
                forecasted_values = smoothed_series

                if intervals is False:
                    result = pd.DataFrame(forecasted_values)
                    result.rename(columns={0:self.data_name}, inplace=True)
                else:

                    series_variance = np.zeros(h)

                    for t in range(h):
                        series_variance[t] = np.dot(np.dot(Z[self.y.shape[0]+t],P[:,:,self.y.shape[0]+t]),Z[self.y.shape[0]+t].T)

                    prediction_05 = forecasted_values - 1.96*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(self.latent_variables.get_z_values()[0]),0.5)
                    prediction_95 = forecasted_values + 1.96*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(self.latent_variables.get_z_values()[0]),0.5)
                    prediction_01 = forecasted_values - 2.575*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(self.latent_variables.get_z_values()[0]),0.5)
                    prediction_99 = forecasted_values + 2.575*np.power(P[0][0][-h:] + self.latent_variables.z_list[0].prior.transform(self.latent_variables.get_z_values()[0]),0.5)

                    result = pd.DataFrame([forecasted_values, prediction_01, prediction_05, 
                        prediction_95, prediction_99]).T
                    result.rename(columns={0:self.data_name, 1: "1% Prediction Interval", 
                        2: "5% Prediction Interval", 3: "95% Prediction Interval", 4: "99% Prediction Interval"}, 
                        inplace=True)
     
                result.index = date_index[-h:]

                return result