class_docstr: Class to hold results from fitting an SARIMAX model.
  
  Parameters
  ----------
  model : SARIMAX instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the SARIMAX model instance.
  polynomial_ar : ndarray
      Array containing autoregressive lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_ma : ndarray
      Array containing moving average lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_seasonal_ar : ndarray
      Array containing seasonal autoregressive lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_seasonal_ma : ndarray
      Array containing seasonal moving average lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_trend : ndarray
      Array containing trend polynomial coefficients, ordered from lowest
      degree to highest. Initialized with ones, unless a coefficient is
      constrained to be zero (in which case it is zero).
  model_orders : list of int
      The orders of each of the polynomials in the model.
  param_terms : list of str
      List of parameters actually included in the model, in sorted order.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","SARIMAX","model",".","Parameters","--","--","--","--","--","model",":","SARIMAX","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","SARIMAX","model","instance",".","polynomial_ar",":","ndarray","Array","containing","autoregressive","lag","polynomial","coefficients",",","ordered","from","lowest","degree","to","highest",".","Initialized","with","ones",",","unless","a","coefficient","is","constrained","to","be","zero","(","in","which","case","it","is","zero",")",".","polynomial_ma",":","ndarray","Array","containing","moving","average","lag","polynomial","coefficients",",","ordered","from","lowest","degree","to","highest",".","Initialized","with","ones",",","unless","a","coefficient","is","constrained","to","be","zero","(","in","which","case","it","is","zero",")",".","polynomial_seasonal_ar",":","ndarray","Array","containing","seasonal","autoregressive","lag","polynomial","coefficients",",","ordered","from","lowest","degree","to","highest",".","Initialized","with","ones",",","unless","a","coefficient","is","constrained","to","be","zero","(","in","which","case","it","is","zero",")",".","polynomial_seasonal_ma",":","ndarray","Array","containing","seasonal","moving","average","lag","polynomial","coefficients",",","ordered","from","lowest","degree","to","highest",".","Initialized","with","ones",",","unless","a","coefficient","is","constrained","to","be","zero","(","in","which","case","it","is","zero",")",".","polynomial_trend",":","ndarray","Array","containing","trend","polynomial","coefficients",",","ordered","from","lowest","degree","to","highest",".","Initialized","with","ones",",","unless","a","coefficient","is","constrained","to","be","zero","(","in","which","case","it","is","zero",")",".","model_orders",":","list","of","int","The","orders","of","each","of","the","polynomials","in","the","model",".","param_terms",":","list","of","str","List","of","parameters","actually","included","in","the","model",",","in","sorted","order",".","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 model specification
      self.specification = Bunch(
          **{
              # Set additional model parameters
              "seasonal_periods": self.model.seasonal_periods,
              "measurement_error": self.model.measurement_error,
              "time_varying_regression": self.model.time_varying_regression,
              "simple_differencing": self.model.simple_differencing,
              "enforce_stationarity": self.model.enforce_stationarity,
              "enforce_invertibility": self.model.enforce_invertibility,
              "hamilton_representation": self.model.hamilton_representation,
              "concentrate_scale": self.model.concentrate_scale,
              "trend_offset": self.model.trend_offset,
              "order": self.model.order,
              "seasonal_order": self.model.seasonal_order,
              # Model order
              "k_diff": self.model.k_diff,
              "k_seasonal_diff": self.model.k_seasonal_diff,
              "k_ar": self.model.k_ar,
              "k_ma": self.model.k_ma,
              "k_seasonal_ar": self.model.k_seasonal_ar,
              "k_seasonal_ma": self.model.k_seasonal_ma,
              # Param Numbers
              "k_ar_params": self.model.k_ar_params,
              "k_ma_params": self.model.k_ma_params,
              # Trend / Regression
              "trend": self.model.trend,
              "k_trend": self.model.k_trend,
              "k_exog": self.model.k_exog,
              "mle_regression": self.model.mle_regression,
              "state_regression": self.model.state_regression,
          }
      )
  
      # Polynomials
      self.polynomial_trend = self.model._polynomial_trend
      self.polynomial_ar = self.model._polynomial_ar
      self.polynomial_ma = self.model._polynomial_ma
      self.polynomial_seasonal_ar = self.model._polynomial_seasonal_ar
      self.polynomial_seasonal_ma = self.model._polynomial_seasonal_ma
      self.polynomial_reduced_ar = np.polymul(
          self.polynomial_ar, self.polynomial_seasonal_ar
      )
      self.polynomial_reduced_ma = np.polymul(
          self.polynomial_ma, self.polynomial_seasonal_ma
      )
  
      # Distinguish parameters
      self.model_orders = self.model.model_orders
      self.param_terms = self.model.param_terms
      start = end = 0
      for name in self.param_terms:
          if name == "ar":
              k = self.model.k_ar_params
          elif name == "ma":
              k = self.model.k_ma_params
          elif name == "seasonal_ar":
              k = self.model.k_seasonal_ar_params
          elif name == "seasonal_ma":
              k = self.model.k_seasonal_ma_params
          else:
              k = self.model_orders[name]
          end += k
          setattr(self, "_params_%s" % name, self.params[start:end])
          start += k
      # GH7527, all terms must be defined
      all_terms = ["ar", "ma", "seasonal_ar", "seasonal_ma", "variance"]
      for name in set(all_terms).difference(self.param_terms):
          setattr(self, "_params_%s" % name, np.empty(0))
  
      # Handle removing data
      self._data_attr_model.extend(["orig_endog", "orig_exog"])
  
imports: from warnings import warn
  import numpy
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
  from statsmodels.tsa.arima.specification import SARIMAXSpecification
  from statsmodels.tsa.arima.params import SARIMAXParams
  from statsmodels.tsa.tsatools import lagmat
  from .initialization import Initialization
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .tools import companion_matrix, diff, is_invertible, constrain_stationary_univariate, unconstrain_stationary_univariate, prepare_exog, prepare_trend_spec, prepare_trend_data
label_desc: Use image node_id 1 to create a new SARIMAXResults object from inherited base classes: MLEResults with example: obj = SARIMAXResults(model, params, filter_results, cov_type)