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: Creates model latent variables
  
  Returns
  ----------
  None (changes model attributes)
docstr_tok: ["Creates","model","latent","variables","Returns","--","--","--","--","--","None","(","changes","model","attributes",")"]
code: def _create_latent_variables(self):
      """Creates model latent variables
  
      Returns
      ----------
      None (changes model attributes)
      """
  
      for p_term in range(self.p):
          self.latent_variables.add_z(
              "p(" + str(p_term + 1) + ")",
              fam.Normal(0, 0.5, transform="logit"),
              fam.Normal(0, 3),
          )
          if p_term == 0:
              self.latent_variables.z_list[-1].start = 3.00
          else:
              self.latent_variables.z_list[-1].start = -4.00
  
      for q_term in range(self.q):
          self.latent_variables.add_z(
              "q(" + str(q_term + 1) + ")",
              fam.Normal(0, 0.5, transform="logit"),
              fam.Normal(0, 3),
          )
          if q_term == 0:
              self.latent_variables.z_list[-1].start = -1.50
          else:
              self.latent_variables.z_list[-1].start = -4.00
  
      self.latent_variables.add_z(
          "v", fam.Flat(transform="exp"), fam.Normal(0, 3)
      )
      self.latent_variables.add_z(
          "GARCH-M", fam.Normal(0, 3, transform=None), fam.Normal(0, 3)
      )
  
      for parm in range(len(self.X_names)):
          self.latent_variables.add_z(
              "Vol Beta " + self.X_names[parm],
              fam.Normal(0, 10, transform=None),
              fam.Normal(0, 3),
          )
  
      for parm in range(len(self.X_names)):
          self.latent_variables.add_z(
              "Returns Beta " + self.X_names[parm],
              fam.Normal(0, 10, transform=None),
              fam.Normal(0, 3),
          )
  
      # Starting values
  
      for i in range(self.p + self.q, self.z_no):
          self.latent_variables.z_list[i].start = 0.0
  
      self.latent_variables.z_list[self.p + self.q].start = 2.0
  
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 2 for calling the EGARCHMReg obj's underlying member method code with example usage: obj._create_latent_variables() without return types