class_docstr: Class to hold results from fitting an unobserved components model.
  
  Parameters
  ----------
  model : UnobservedComponents instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the unobserved components
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","unobserved","components","model",".","Parameters","--","--","--","--","--","model",":","UnobservedComponents","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","unobserved","components","model","instance",".","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
  
      # Save _init_kwds
      self._init_kwds = self.model._get_init_kwds()
  
      # Save number of states by type
      self._k_states_by_type = {
          "seasonal": self.model._k_seasonal_states,
          "freq_seasonal": self.model._k_freq_seas_states,
          "cycle": self.model._k_cycle_states,
      }
  
      # Save the model specification
      self.specification = Bunch(
          **{
              # Model options
              "level": self.model.level,
              "trend": self.model.trend,
              "seasonal_periods": self.model.seasonal_periods,
              "seasonal": self.model.seasonal,
              "freq_seasonal": self.model.freq_seasonal,
              "freq_seasonal_periods": self.model.freq_seasonal_periods,
              "freq_seasonal_harmonics": self.model.freq_seasonal_harmonics,
              "cycle": self.model.cycle,
              "ar_order": self.model.ar_order,
              "autoregressive": self.model.autoregressive,
              "irregular": self.model.irregular,
              "stochastic_level": self.model.stochastic_level,
              "stochastic_trend": self.model.stochastic_trend,
              "stochastic_seasonal": self.model.stochastic_seasonal,
              "stochastic_freq_seasonal": self.model.stochastic_freq_seasonal,
              "stochastic_cycle": self.model.stochastic_cycle,
              "damped_cycle": self.model.damped_cycle,
              "regression": self.model.regression,
              "mle_regression": self.model.mle_regression,
              "k_exog": self.model.k_exog,
              # Check for string trend/level specification
              "trend_specification": self.model.trend_specification,
          }
      )
  
imports: from warnings import warn
  import numpy
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import OutputWarning, SpecificationWarning
  import statsmodels.base.wrapper
  from statsmodels.tsa.filters.hp_filter import hpfilter
  from statsmodels.tsa.tsatools import lagmat
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .initialization import Initialization
  from .tools import companion_matrix, constrain_stationary_univariate, unconstrain_stationary_univariate, prepare_exog
label_desc: Use image node_id 1 to create a new UnobservedComponentsResults object from inherited base classes: MLEResults with example: obj = UnobservedComponentsResults(model, params, filter_results, cov_type)