class_docstr: Inherits time series methods from TSM parent class.
  
  **** skew BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate 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","parent","class",".","*","*","*","*","skew","BETA-t-EGARCH","IN","MEAN","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","univariate","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: 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(
              [
                  fam.Skewt.draw_variable(
                      loc=self.latent_variables.z_list[
                          -2
                      ].prior.transform(lv_draws[-2, i]),
                      shape=self.latent_variables.z_list[
                          -3
                      ].prior.transform(lv_draws[-3, i]),
                      skewness=self.latent_variables.z_list[
                          -4
                      ].prior.transform(lv_draws[-4, i]),
                      nsims=len(sigmas[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
  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 18 for calling the SEGARCHM obj's underlying member method code with example usage: obj.sample(nsims) and returns: data_draws