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: Samples from the posterior predictive distribution
  
  Parameters
  ----------
  nsims : int (default : 1000)
      How many draws from the posterior predictive distribution
  
  Returns
  ----------
  - np.ndarray of draws from the data
docstr_tok: ["Samples","from","the","posterior","predictive","distribution","Parameters","--","--","--","--","--","nsims",":","int","(","default",":","1000",")","How","many","draws","from","the","posterior","predictive","distribution","Returns","--","--","--","--","--","-","np.ndarray","of","draws","from","the","data"]
code: def sample(self, nsims=1000):
      """Samples from the posterior predictive distribution
  
      Parameters
      ----------
      nsims : int (default : 1000)
          How many draws from the posterior predictive distribution
  
      Returns
      ----------
      - np.ndarray of draws from the data
      """
      if self.latent_variables.estimation_method not in ["BBVI", "M-H"]:
          raise Exception("No latent variables estimated!")
      else:
          lv_draws = self.draw_latent_variables(nsims=nsims)
          sigmas = [
              self._model(lv_draws[:, i])[0] for i in range(nsims)
          ]
          data_draws = np.array(
              [
                  ss.t.rvs(
                      loc=self.latent_variables.z_list[
                          -len(self.X_names) * 2 - 1
                      ].prior.transform(
                          lv_draws[-len(self.X_names) * 2 - 1, i]
                      ),
                      df=self.latent_variables.z_list[
                          -len(self.X_names) * 2 - 2
                      ].prior.transform(
                          lv_draws[-len(self.X_names) * 2 - 2, i]
                      ),
                      scale=np.exp(sigmas[i] / 2.0),
                  )
                  for i in range(nsims)
              ]
          )
          return data_draws
  
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 18 for calling the EGARCHMReg obj's underlying member method code with example usage: obj.sample(nsims) and returns: data_draws