code: def predict_is(self, h=5, fit_once=True, fit_method='MLE', intervals=False):
               
  
          predictions = []
  
          for t in range(0,h):
              data1 = self.data_original.iloc[:-h+t,:]
              data2 = self.data_original.iloc[-h+t:,:]
              x = DynReg(formula=self.formula, data=data1)
  
              if fit_once is False:
                  x.fit(printer=False, fit_method=fit_method)
              if t == 0:
                  if fit_once is True:
                      x.fit(printer=False, fit_method=fit_method)
                      saved_lvs = x.latent_variables
                  predictions = x.predict(1, oos_data=data2, intervals=intervals)
              else:
                  if fit_once is True:
                      x.latent_variables = saved_lvs
                  predictions = pd.concat([predictions,x.predict(h=1, oos_data=data2, intervals=intervals)])
          
          predictions.rename(columns={0:self.y_name}, inplace=True)
          predictions.index = self.index[-h:]
  
          return predictions
docs: Makes dynamic in-sample predictions with the estimated model
  
          Parameters
          ----------
          h : int (default : 5)
              How many steps would you like to forecast?
  
          fit_once : boolean
              (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
  
          fit_method : string
              Which method to fit the model with
  
          intervals : boolean
              Whether to output prediction intervals or not
  
          Returns
          ----------
          - pd.DataFrame with predicted values