class_docstr: Significance test for continuous variables in a nonparametric regression.
  
  The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis
  is ``dE(Y|X)/dX_not_i != 0``.
  
  Parameters
  ----------
  model : KernelReg instance
      This is the nonparametric regression model whose elements
      are tested for significance.
  test_vars : tuple, list of integers, array_like
      index of position of the continuous variables to be tested
      for significance. E.g. (1,3,5) jointly tests variables at
      position 1,3 and 5 for significance.
  nboot : int
      Number of bootstrap samples used to determine the distribution
      of the test statistic in a finite sample. Default is 400
  nested_res : int
      Number of nested resamples used to calculate lambda.
      Must enable the pivot option
  pivot : bool
      Pivot the test statistic by dividing by its standard error
      Significantly increases computational time. But pivot statistics
      have more desirable properties
      (See references)
  
  Attributes
  ----------
  sig : str
      The significance level of the variable(s) tested
      "Not Significant": Not significant at the 90% confidence level
                          Fails to reject the null
      "*": Significant at the 90% confidence level
      "**": Significant at the 95% confidence level
      "***": Significant at the 99% confidence level
  
  Notes
  -----
  This class allows testing of joint hypothesis as long as all variables
  are continuous.
  
  References
  ----------
  Racine, J.: "Consistent Significance Testing for Nonparametric Regression"
  Journal of Business & Economics Statistics.
  
  Chapter 12 in [1].
docstr: Computes the significance value for the variable(s) tested.
  
  The empirical distribution of the test statistic is obtained through
  bootstrapping the sample.  The null hypothesis is rejected if the test
  statistic is larger than the 90, 95, 99 percentiles.
code: def _compute_sig(self):
      """
      Computes the significance value for the variable(s) tested.
  
      The empirical distribution of the test statistic is obtained through
      bootstrapping the sample.  The null hypothesis is rejected if the test
      statistic is larger than the 90, 95, 99 percentiles.
      """
      t_dist = np.empty(shape=(self.nboot,))
      Y = self.endog
      X = copy.deepcopy(self.exog)
      n = np.shape(Y)[0]
  
      X[:, self.test_vars] = np.mean(X[:, self.test_vars], axis=0)
      # Calculate the restricted mean. See p. 372 in [8]
      M = KernelReg(
          Y,
          X,
          self.var_type,
          self.model.reg_type,
          self.bw,
          defaults=EstimatorSettings(efficient=False),
      ).fit()[0]
      M = np.reshape(M, (n, 1))
      e = Y - M
      e = e - np.mean(e)  # recenter residuals
      for i in range(self.nboot):
          ind = np.random.randint(0, n, size=(n, 1))
          e_boot = e[ind, 0]
          Y_boot = M + e_boot
          t_dist[i] = self._compute_test_stat(Y_boot, self.exog)
  
      self.t_dist = t_dist
      sig = "Not Significant"
      if self.test_stat > mquantiles(t_dist, 0.9):
          sig = "*"
      if self.test_stat > mquantiles(t_dist, 0.95):
          sig = "**"
      if self.test_stat > mquantiles(t_dist, 0.99):
          sig = "***"
  
      return sig
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from ._kernel_base import GenericKDE, EstimatorSettings, gpke, LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR, kernel_func
label_desc: Use image node_id 6 for calling the TestRegCoefC obj's underlying member method code with example usage: obj._compute_sig() and returns: sig