class_docstr: Class to hold results from fitting an DynamicFactor model.
  
  Parameters
  ----------
  model : DynamicFactor instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the DynamicFactor model
      instance.
  coefficient_matrices_var : ndarray
      Array containing autoregressive lag polynomial coefficient matrices,
      ordered from lowest degree to highest.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","DynamicFactor","model",".","Parameters","--","--","--","--","--","model",":","DynamicFactor","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","DynamicFactor","model","instance",".","coefficient_matrices_var",":","ndarray","Array","containing","autoregressive","lag","polynomial","coefficient","matrices",",","ordered","from","lowest","degree","to","highest",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
code: def __init__(
      self, model, params, filter_results, cov_type=None, **kwargs
  ):
      super().__init__(
          model, params, filter_results, cov_type, **kwargs
      )
  
      self.df_resid = np.inf  # attribute required for wald tests
  
      self.specification = Bunch(
          **{
              # Model properties
              "k_endog": self.model.k_endog,
              "enforce_stationarity": self.model.enforce_stationarity,
              # Factor-related properties
              "k_factors": self.model.k_factors,
              "factor_order": self.model.factor_order,
              # Error-related properties
              "error_order": self.model.error_order,
              "error_var": self.model.error_var,
              "error_cov_type": self.model.error_cov_type,
              # Other properties
              "k_exog": self.model.k_exog,
          }
      )
  
      # Polynomials / coefficient matrices
      self.coefficient_matrices_var = None
      if self.model.factor_order > 0:
          ar_params = np.array(
              self.params[self.model._params_factor_transition]
          )
          k_factors = self.model.k_factors
          factor_order = self.model.factor_order
          self.coefficient_matrices_var = (
              (ar_params.reshape(k_factors * factor_order, k_factors).T)
              .reshape(k_factors, k_factors, factor_order)
              .T
          )
  
      self.coefficient_matrices_error = None
      if self.model.error_order > 0:
          ar_params = np.array(
              self.params[self.model._params_error_transition]
          )
          k_endog = self.model.k_endog
          error_order = self.model.error_order
          if self.model.error_var:
              self.coefficient_matrices_error = (
                  (ar_params.reshape(k_endog * error_order, k_endog).T)
                  .reshape(k_endog, k_endog, error_order)
                  .T
              )
          else:
              mat = np.zeros((k_endog, k_endog * error_order))
              mat[self.model._idx_error_diag] = ar_params
              self.coefficient_matrices_error = mat.T.reshape(
                  error_order, k_endog, k_endog
              )
  
imports: import numpy
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .tools import is_invertible, prepare_exog, constrain_stationary_univariate, unconstrain_stationary_univariate, constrain_stationary_multivariate, unconstrain_stationary_multivariate
  from statsmodels.multivariate.pca import PCA
  from statsmodels.regression.linear_model import OLS
  from statsmodels.tsa.vector_ar.var_model import VAR
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.tsatools import lagmat
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
  from statsmodels.compat.pandas import Appender
label_desc: Use image node_id 1 to create a new DynamicFactorResults object from inherited base classes: MLEResults with example: obj = DynamicFactorResults(model, params, filter_results, cov_type)