class_docstr: A Results class for empirical likelihood regression through the origin.
  
  Parameters
  ----------
  model : class
      An OLS model with an intercept.
  
  params : 1darray
      Fitted parameters.
  
  est_llr : float
      The log likelihood ratio of the model with the intercept restricted to
      0 at the maximum likelihood estimates of the parameters.
      llr_restricted/llr_unrestricted
  
  llf_el : float
      The log likelihood of the fitted model with the intercept restricted to 0.
  
  Attributes
  ----------
  model : class
      An OLS model with an intercept.
  
  params : 1darray
      Fitted parameter.
  
  llr : float
      The log likelihood ratio of the maximum empirical likelihood estimate.
  
  llf_el : float
      The log likelihood of the fitted model with the intercept restricted to 0.
  
  Notes
  -----
  IMPORTANT.  Since EL estimation does not drop the intercept parameter but
  instead estimates the slope parameters conditional on the slope parameter
  being 0, the first element for params will be the intercept, which is
  restricted to 0.
  
  IMPORTANT.  This class inherits from RegressionResults but inference is
  conducted via empirical likelihood.  Therefore, any methods that
  require an estimate of the covariance matrix will not function.  Instead
  use el_test and conf_int_el to conduct inference.
  
  Examples
  --------
  >>> import statsmodels.api as sm
  >>> data = sm.datasets.bc.load()
  >>> model = sm.emplike.ELOriginRegress(data.endog, data.exog)
  >>> fitted = model.fit()
  >>> fitted.params #  0 is the intercept term.
  array([ 0.        ,  0.00351813])
  
  >>> fitted.el_test(np.array([.0034]), np.array([1]))
  (3.6696503297979302, 0.055411808127497755)
  >>> fitted.conf_int_el(1)
  (0.0033971871114706867, 0.0036373150174892847)
  
  # No covariance matrix so normal inference is not valid
  >>> fitted.conf_int()
  TypeError: unsupported operand type(s) for *: 'instancemethod' and 'float'
code: def __init__(self, model, params, est_llr, llf_el):
      self.model = model
      self.params = np.squeeze(params)
      self.llr = est_llr
      self.llf_el = llf_el
  
file_path: statsmodels/statsmodels/emplike/originregress.py
imports: import numpy
  from scipy import optimize
  from scipy.stats import chi2
  from statsmodels.regression.linear_model import OLS, RegressionResults
  from statsmodels.tools.tools import add_constant
label_desc: Use image node_id 1 to create a new OriginResults object from inherited base classes: RegressionResults with example: obj = OriginResults(model, params, est_llr, llf_el)