code: def predict_is(self,h=5):
               
  
          predictions = []
  
          for t in range(0,h):
              data1 = self.data_original.iloc[:-h+t,:]
              data2 = self.data_original.iloc[-h+t:,:] 
              x = NDynReg(formula=self.formula, data=data1, family=self.family)                                       
              x.fit(print_progress=False)
              if t == 0:
                  predictions = x.predict(1,oos_data=data2)
              else:
                  predictions = pd.concat([predictions,x.predict(1,oos_data=data2)])
          
          predictions.rename(columns={0:self.data_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?
  
          Returns
          ----------
          - pd.DataFrame with predicted values