class_docstr: Inherits time series methods from TSM class.
  
  **** skew 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",".","*","*","*","*","skew","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: Plots the fit of the model
  
  Returns
  ----------
  None (plots data and the fit)
docstr_tok: ["Plots","the","fit","of","the","model","Returns","--","--","--","--","--","None","(","plots","data","and","the","fit",")"]
code: def plot_fit(self, **kwargs):
      """Plots the fit of the model
  
      Returns
      ----------
      None (plots data and the fit)
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      if self.latent_variables.estimated is False:
          raise Exception("No latent variables estimated!")
      else:
          t_params = self.transform_z()
          plt.figure(figsize=figsize)
          date_index = self.index[max(self.p, self.q) :]
          sigma2, Y, ___, theta = self._model(
              self.latent_variables.get_z_values()
          )
          plt.plot(
              date_index,
              np.abs(Y - theta),
              label=self.data_name + " Absolute Demeaned Values",
          )
          plt.plot(
              date_index,
              np.exp(sigma2 / 2.0),
              label="SEGARCH("
              + str(self.p)
              + ","
              + str(self.q)
              + ") Conditional Volatility",
              c="black",
          )
          plt.title(self.data_name + " Volatility Plot")
          plt.legend(loc=2)
          plt.show()
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  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 13 for calling the SEGARCH obj's underlying member method code with example usage: obj.plot_fit() without return types