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: 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(
          ss.t.logpdf(
              x=Y,
              df=self.latent_variables.z_list[
                  -(len(self.X_names) * 2) - 2
              ].prior.transform(beta[-(len(self.X_names) * 2) - 2]),
              loc=theta,
              scale=np.exp(lmda / 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 11 for calling the EGARCHMReg obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: unknown