class_docstr: Class to hold results from fitting an VARMAX model.
  
  Parameters
  ----------
  model : VARMAX instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the VARMAX model instance.
  coefficient_matrices_var : ndarray
      Array containing autoregressive lag polynomial coefficient matrices,
      ordered from lowest degree to highest.
  coefficient_matrices_vma : ndarray
      Array containing moving average lag polynomial coefficients,
      ordered from lowest degree to highest.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
docstr: Set the final state intercept value using out-of-sample `exog` / trend
  
  Parameters
  ----------
  exog : ndarray
      Out-of-sample `exog` values, usually produced by
      `_validate_out_of_sample_exog` to ensure the correct shape (this
      method does not do any additional validation of its own).
  out_of_sample : int
      Number of out-of-sample periods.
  
  Notes
  -----
  This context manager calls the model-level context manager and
  additionally updates the last element of filter_results.state_intercept
  appropriately.
code: def _set_final_exog(self, exog):
      """
      Set the final state intercept value using out-of-sample `exog` / trend
  
      Parameters
      ----------
      exog : ndarray
          Out-of-sample `exog` values, usually produced by
          `_validate_out_of_sample_exog` to ensure the correct shape (this
          method does not do any additional validation of its own).
      out_of_sample : int
          Number of out-of-sample periods.
  
      Notes
      -----
      This context manager calls the model-level context manager and
      additionally updates the last element of filter_results.state_intercept
      appropriately.
      """
      mod = self.model
      with mod._set_final_exog(exog):
          cache_value = self.filter_results.state_intercept[:, -1]
          mod.update(self.params)
          self.filter_results.state_intercept[: mod.k_endog, -1] = mod[
              "state_intercept", : mod.k_endog, -1
          ]
          try:
              yield
          finally:
              self.filter_results.state_intercept[:, -1] = cache_value
  
imports: import contextlib
  from warnings import warn
  import pandas
  import numpy
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.vector_ar import var_model
  import statsmodels.base.wrapper
  from statsmodels.tools.sm_exceptions import EstimationWarning
  from .kalman_filter import INVERT_UNIVARIATE, SOLVE_LU
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .initialization import Initialization
  from .tools import is_invertible, concat, prepare_exog, constrain_stationary_multivariate, unconstrain_stationary_multivariate, prepare_trend_spec, prepare_trend_data
label_desc: Use image node_id 3 for calling the VARMAXResults obj's underlying member method code with example usage: obj._set_final_exog(exog) without return types