class_docstr: Class to hold results from fitting a recursive least squares model.
  
  Parameters
  ----------
  model : RecursiveLS instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the recursive least squares
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","a","recursive","least","squares","model",".","Parameters","--","--","--","--","--","model",":","RecursiveLS","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","recursive","least","squares","model","instance",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
docstr: Cumulative sum of squares of standardized recursive residuals
  statistics
  
  Returns
  -------
  cusum_squares : array_like
      An array of length `nobs - k_exog` holding the
      CUSUM of squares statistics.
  
  Notes
  -----
  The CUSUM of squares statistic takes the form:
  
  .. math::
  
      s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg /
            \left ( \sum_{j=k+1}^T w_j^2 \right )
  
  where :math:`w_j` is the recursive residual at time :math:`j`.
  
  Excludes the first `k_exog` datapoints.
  
  References
  ----------
  .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
     "Techniques for Testing the Constancy of
     Regression Relationships over Time."
     Journal of the Royal Statistical Society.
     Series B (Methodological) 37 (2): 149-92.
docstr_tok: ["Cumulative","sum","of","squares","of","standardized","recursive","residuals","statistics","Returns","--","--","--","-","cusum_squares",":","array_like","An","array","of","length","`","nobs","-","k_exog","`","holding","the","CUSUM","of","squares","statistics",".","Notes","--","--","-","The","CUSUM","of","squares","statistic","takes","the","form",":","..","math",":",":","s_t","=","\\left","(","\\sum_","{","j=k+1","}","^t","w_j^2","\\right",")","\\Bigg","\/","\\left","(","\\sum_","{","j=k+1","}","^T","w_j^2","\\right",")","where",":","math",":","`","w_j","`","is","the","recursive","residual","at","time",":","math",":","`","j","`",".","Excludes","the","first","`","k_exog","`","datapoints",".","References","--","--","--","--","--","..","[","*","]","Brown",",","R.","L.",",","J.","Durbin",",","and","J.","M.","Evans",".","1975",".","``","Techniques","for","Testing","the","Constancy","of","Regression","Relationships","over","Time",".","''","Journal","of","the","Royal","Statistical","Society",".","Series","B","(","Methodological",")","37","(","2",")",":","149-92","."]
code: def cusum_squares(self):
      r"""
      Cumulative sum of squares of standardized recursive residuals
      statistics
  
      Returns
      -------
      cusum_squares : array_like
          An array of length `nobs - k_exog` holding the
          CUSUM of squares statistics.
  
      Notes
      -----
      The CUSUM of squares statistic takes the form:
  
      .. math::
  
          s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg /
                \left ( \sum_{j=k+1}^T w_j^2 \right )
  
      where :math:`w_j` is the recursive residual at time :math:`j`.
  
      Excludes the first `k_exog` datapoints.
  
      References
      ----------
      .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
         "Techniques for Testing the Constancy of
         Regression Relationships over Time."
         Journal of the Royal Statistical Society.
         Series B (Methodological) 37 (2): 149-92.
      """
      d = max(self.nobs_diffuse, self.loglikelihood_burn)
      numer = np.cumsum(self.resid_recursive[d:] ** 2)
      denom = numer[-1]
      return numer / denom
  
imports: import numpy
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.statespace.mlemodel import MLEModel, MLEResults, MLEResultsWrapper, PredictionResults, PredictionResultsWrapper
  from statsmodels.tsa.statespace.tools import concat
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
label_desc: Use image node_id 5 for calling the RecursiveLSResults obj's underlying member method code with example usage: obj.cusum_squares() and returns: unknown