class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH) 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 ARCH 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(
              [
                  ss.norm.rvs(
                      loc=self.latent_variables.z_list[
                          -1
                      ].prior.transform(lv_draws[-1, i]),
                      scale=np.sqrt(sigmas[i]),
                  )
                  for i in range(nsims)
              ]
          )
          return data_draws
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .garch_recursions import garch_recursion
label_desc: Use image node_id 17 for calling the GARCH obj's underlying member method code with example usage: obj.sample(nsims) and returns: data_draws