class_docstr: Inherits time series methods from TSM class.
  
  **** DYNAMIC AUTOREGRESSIVE MODEL ****
  
  Parameters
  ----------
  
  ar : int
      Number of autoregressive lags
  
  data : pd.DataFrame
      Field to specify the data that will be used
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","DYNAMIC","AUTOREGRESSIVE","MODEL","*","*","*","*","Parameters","--","--","--","--","--","ar",":","int","Number","of","autoregressive","lags","data",":","pd.DataFrame","Field","to","specify","the","data","that","will","be","used"]
docstr: Creates forecasted states and variances
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  a : np.ndarray
      Forecasted states
  
  P : np.ndarray
      Variance of forecasted states
docstr_tok: ["Creates","forecasted","states","and","variances","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","a",":","np.ndarray","Forecasted","states","P",":","np.ndarray","Variance","of","forecasted","states"]
code: def _forecast_model(self, beta, Z, h):
      """Creates forecasted states and variances
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      a : np.ndarray
          Forecasted states
  
      P : np.ndarray
          Variance of forecasted states
      """
  
      T, _, R, Q, H = self._ss_matrices(beta)
      return dl_univariate_kalman_fcst(self.data, Z, H, T, Q, R, 0.0, h)
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 4 for calling the DAR obj's underlying member method code with example usage: obj._forecast_model(beta, Z, h) and returns: dl_univariate_kalman_fcst