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: Adds leverage term to the model
  
  Returns
  ----------
  None (changes instance attributes)
docstr_tok: ["Adds","leverage","term","to","the","model","Returns","--","--","--","--","--","None","(","changes","instance","attributes",")"]
code: def add_leverage(self):
      """Adds leverage term to the model
  
      Returns
      ----------
      None (changes instance attributes)
      """
  
      if self.leverage is True:
          pass
      else:
          self.leverage = True
          self.z_no += 1
  
          for i in range(len(self.X_names) * 2 + 3):
              self.latent_variables.z_list.pop()
  
          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),
              )
  
          self.latent_variables.add_z(
              "Leverage Term",
              fam.Flat(transform=None),
              fam.Normal(0, 3),
          )
          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),
          )
          self.latent_variables.z_list[-3].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 10 for calling the EGARCHMReg obj's underlying member method code with example usage: obj.add_leverage() without return types