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
            Whether to fit the model once at the beginning (True), or fit every iteration (False)

        fit_method : string
            Which method to fit the model with

        Returns
        ----------
        - pd.DataFrame with predicted values

def predict_is(self, h=5, fit_once=False, fit_method='OLS', **kwargs):
        """ 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
            Whether to fit the model once at the beginning (True), or fit every iteration (False)

        fit_method : string
            Which method to fit the model with

        Returns
        ----------
        - pd.DataFrame with predicted values
        """     

        iterations = kwargs.get('iterations', 1000)

        predictions = []

        for t in range(0,h):
            new_data = self.data_original.iloc[:-h+t]
            x = VAR(lags=self.lags, integ=self.integ, data=new_data)
            
            if fit_once is False:
                if fit_method == 'BBVI':
                    x.fit(fit_method='BBVI', iterations=iterations)
                else:
                    x.fit(fit_method=fit_method)

            if t == 0:

                if fit_once is True:
                    if fit_method == 'BBVI':
                        x.fit(fit_method='BBVI', iterations=iterations)
                    else:
                        x.fit(fit_method=fit_method)
                    saved_lvs = x.latent_variables

                predictions = x.predict(1)
            else:
                if fit_once is True:
                    x.latent_variables = saved_lvs
                    
                predictions = pd.concat([predictions,x.predict(1)])
        
        #predictions.rename(columns={0:self.data_name}, inplace=True)
        predictions.index = self.index[-h:]

        return predictions