code: def __init__(self, endog, **kwargs):
      # Setup the model as if we had a factor order of 4
      super().__init__(
          endog, k_factors=1, factor_order=4, error_order=2, **kwargs
      )
  
      # Note: `self.parameters` is an ordered dict with the
      # keys corresponding to parameter types, and the values
      # the number of parameters of that type.
      # Add the new parameters
      self.parameters["new_loadings"] = 3
  
      # Cache a slice for the location of the 4 factor AR
      # parameters (a_1, ..., a_4) in the full parameter vector
      offset = (
          self.parameters["factor_loadings"]
          + self.parameters["exog"]
          + self.parameters["error_cov"]
      )
      self._params_factor_ar = np.s_[offset : offset + 2]
      self._params_factor_zero = np.s_[offset + 2 : offset + 4]
  
file_path: statsmodels/examples/python/statespace_dfm_coincident.py
imports: import numpy
  import pandas
  import statsmodels.api
  import matplotlib.pyplot
  from pandas_datareader.data import DataReader
  from statsmodels.tsa.statespace import tools
label_desc: Use image node_id 1 to create a new ExtendedDFM object from inherited base classes: sm.tsa with example: obj = ExtendedDFM(endog)