class_docstr: Inherits time series methods from TSM class.
  
  **** BETA-t-EGARCH IN MEAN REGRESSION MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many SCORE terms the model will have.
  
  formula : string
      patsy string describing the regression
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","BETA-t-EGARCH","IN","MEAN","REGRESSION","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","Field","to","specify","the","time","series","data","that","will","be","used",".","p",":","int","Field","to","specify","how","many","GARCH","terms","the","model","will","have",".","q",":","int","Field","to","specify","how","many","SCORE","terms","the","model","will","have",".","formula",":","string","patsy","string","describing","the","regression"]
docstr: 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 return prediction intervals
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["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","return","prediction","intervals","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict_is(
      self,
      h=5,
      fit_once=True,
      fit_method="MLE",
      intervals=False,
      **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
          (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 return prediction intervals
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      predictions = []
  
      for t in range(0, h):
          data1 = self.data_original.iloc[: -h + t, :]
          data2 = self.data_original.iloc[-h + t :, :]
          x = EGARCHMReg(
              p=self.p,
              q=self.q,
              data=self.data_original[: (-h + t)],
              formula=self.formula,
          )
  
          if fit_once is False:
              x.fit(method=fit_method, printer=False)
  
          if t == 0:
              if fit_once is True:
                  x.fit(method=fit_method, printer=False)
                  saved_lvs = x.latent_variables
              predictions = x.predict(
                  h=1, intervals=intervals, oos_data=data2
              )
          else:
              if fit_once is True:
                  x.latent_variables = saved_lvs
              predictions = pd.concat(
                  [
                      predictions,
                      x.predict(
                          h=1, intervals=intervals, oos_data=data2
                      ),
                  ]
              )
  
      predictions.rename(columns={0: self.y_name}, inplace=True)
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import gas
  from ..None import data_check
label_desc: Use image node_id 15 for calling the EGARCHMReg obj's underlying member method code with example usage: obj.predict_is(h, fit_once, fit_method, intervals) and returns: predictions