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
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docstr: Estimates of unobserved factors
  
  Returns
  -------
  out : Bunch
      Has the following attributes shown in Notes.
  
  Notes
  -----
  The output is a bunch of the following format:
  
  - `filtered`: a time series array with the filtered estimate of
    the component
  - `filtered_cov`: a time series array with the filtered estimate of
    the variance/covariance of the component
  - `smoothed`: a time series array with the smoothed estimate of
    the component
  - `smoothed_cov`: a time series array with the smoothed estimate of
    the variance/covariance of the component
  - `offset`: an integer giving the offset in the state vector where
    this component begins
code: def factors(self):
      """
      Estimates of unobserved factors
  
      Returns
      -------
      out : Bunch
          Has the following attributes shown in Notes.
  
      Notes
      -----
      The output is a bunch of the following format:
  
      - `filtered`: a time series array with the filtered estimate of
        the component
      - `filtered_cov`: a time series array with the filtered estimate of
        the variance/covariance of the component
      - `smoothed`: a time series array with the smoothed estimate of
        the component
      - `smoothed_cov`: a time series array with the smoothed estimate of
        the variance/covariance of the component
      - `offset`: an integer giving the offset in the state vector where
        this component begins
      """
      # If present, level is always the first component of the state vector
      out = None
      spec = self.specification
      if spec.k_factors > 0:
          offset = 0
          end = spec.k_factors
          res = self.filter_results
          out = Bunch(
              filtered=res.filtered_state[offset:end],
              filtered_cov=res.filtered_state_cov[
                  offset:end, offset:end
              ],
              smoothed=None,
              smoothed_cov=None,
              offset=offset,
          )
          if self.smoothed_state is not None:
              out.smoothed = self.smoothed_state[offset:end]
          if self.smoothed_state_cov is not None:
              out.smoothed_cov = self.smoothed_state_cov[
                  offset:end, offset:end
              ]
      return out
  
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 2 for calling the DynamicFactorResults obj's underlying member method code with example usage: obj.factors() and returns: out