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.
class_docstr_tok: ["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","."]
docstr: Adds a leverage term to the model to account for the asymmetric effect of new information on volatility
  
  Returns
  ----------
  None (changes instance attributes)
docstr_tok: ["Adds","a","leverage","term","to","the","model","to","account","for","the","asymmetric","effect","of","new","information","on","volatility","Returns","--","--","--","--","--","None","(","changes","instance","attributes",")"]
code: def add_leverage(self):
      """Adds a leverage term to the model to account for the asymmetric effect of new information on volatility
  
      Returns
      ----------
      None (changes instance attributes)
      """
  
      if self.leverage is True:
          pass
      else:
          self.leverage = True
          self.z_no += 1
          self.latent_variables.z_list.pop()
          self.latent_variables.z_list.pop()
          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.z_list[-2].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 10 for calling the EGARCH obj's underlying member method code with example usage: obj.add_leverage() without return types