class_docstr: Inherits time series methods from TSM class.
  
  **** BETA-t-EGARCH 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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code: def __init__(self, data, p, q, target=None):
      # Initialize TSM object
      super(EGARCH, self).__init__("EGARCH")
  
      # Latent Variables
      self.p = p
      self.q = q
      self.z_no = self.p + self.q + 3
      self.max_lag = max(self.p, self.q)
      self.leverage = False
      self.model_name = (
          "EGARCH(" + str(self.p) + "," + str(self.q) + ")"
      )
      self._z_hide = (
          0  # Whether to cutoff latent variables from the results table
      )
      self.supported_methods = ["MLE", "PML", "Laplace", "M-H", "BBVI"]
      self.default_method = "MLE"
      self.multivariate_model = False
  
      # Format the data
      (
          self.data,
          self.data_name,
          self.is_pandas,
          self.index,
      ) = dc.data_check(data, target)
      self.data_length = self.data.shape[0]
      self._create_latent_variables()
  
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 1 to create a new EGARCH object from inherited base classes: tsm with example: obj = EGARCH(data, p, q, target)