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: Creates the negative log-likelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative logliklihood of the model
docstr_tok: ["Creates","the","negative","log-likelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","logliklihood","of","the","model"]
code: def neg_loglik(self, beta):
      """Creates the negative log-likelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative logliklihood of the model
      """
  
      lmda, Y, ___, theta = self._model(beta)
      return -np.sum(
          logpdf(
              Y,
              self.latent_variables.z_list[-2].prior.transform(
                  beta[-2]
              ),
              loc=theta,
              scale=np.exp(lmda / 2.0),
              skewness=self.latent_variables.z_list[-3].prior.transform(
                  beta[-3]
              ),
          )
      )
  
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 11 for calling the SEGARCH obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: unknown