class_docstr: Inherits time series methods from TSM class.
  
  **** BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      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.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
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docstr: Makes dynamic out-of-sample predictions with the model on in-sample data
  
  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","out-of-sample","predictions","with","the","model","on","in-sample","data","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 out-of-sample predictions with the model on in-sample data
  
      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):
          x = EGARCHM(p=self.p, q=self.q, data=self.data[: -h + t])
  
          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(1, intervals=intervals)
          else:
              if fit_once is True:
                  x.latent_variables = saved_lvs
              predictions = pd.concat(
                  [predictions, x.predict(1, intervals=intervals)]
              )
  
      if intervals is True:
          predictions.rename(
              columns={
                  0: self.data_name,
                  1: "1% Prediction Interval",
                  2: "5% Prediction Interval",
                  3: "95% Prediction Interval",
                  4: "99% Prediction Interval",
              },
              inplace=True,
          )
      else:
          predictions.rename(columns={0: self.data_name}, inplace=True)
  
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  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 EGARCHM obj's underlying member method code with example usage: obj.predict_is(h, fit_once, fit_method, intervals) and returns: predictions