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.
class_docstr_tok: ["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","."]
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)
      """
  
      self.latent_variables.add_z(
          "Vol Constant",
          fam.Normal(0, 3, transform=None),
          fam.Normal(0, 3),
      )
  
      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(
          "Returns Constant",
          fam.Normal(0, 3, transform=None),
          fam.Normal(0, 3),
      )
      self.latent_variables.add_z(
          "GARCH-M", fam.Normal(0, 3, transform=None), fam.Normal(0, 3)
      )
  
      # Starting values
      self.latent_variables.z_list[-3].start = 2.0
  
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 2 for calling the EGARCHM obj's underlying member method code with example usage: obj._create_latent_variables() without return types