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"]
code: def __init__(self, data, p, q, formula):
      # Initialize TSM object
      super(EGARCHMReg, self).__init__("EGARCHMReg")
  
      # Latent variables
      self.p = p
      self.q = q
      self.max_lag = max(self.p, self.q)
      self.z_no = self.p + self.q + 2
      self._z_hide = (
          0  # Whether to cutoff variance latent variables from results
      )
      self.supported_methods = ["MLE", "PML", "Laplace", "M-H", "BBVI"]
      self.default_method = "MLE"
      self.multivariate_model = False
      self.leverage = False
      self.model_name = (
          "EGARCHMReg(" + str(self.p) + "," + str(self.q) + ")"
      )
  
      # Format the data
      self.is_pandas = True  # This is compulsory for this model type
      self.data_original = data
      self.formula = formula
      self.y, self.X = dmatrices(formula, data)
      self.z_no += self.X.shape[1] * 2
      self.y_name = self.y.design_info.describe()
      self.data_name = self.y_name
      self.X_names = self.X.design_info.describe().split(" + ")
      self.y = np.array([self.y]).ravel()
      self.data = self.y
      self.data_length = len(self.data)
      self.X = np.array([self.X])[0]
      self.index = data.index
      self.initial_values = np.zeros(self.z_no)
  
      self._create_latent_variables()
  
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 1 to create a new EGARCHMReg object from inherited base classes: tsm with example: obj = EGARCHMReg(data, p, q, formula)