code: def cov_nw_groupsum(
      results,
      nlags,
      time,
      weights_func=weights_bartlett,
      use_correction=0,
  ):
      """Driscoll and Kraay Panel robust covariance matrix
  
      Robust covariance matrix for panel data of Driscoll and Kraay.
  
      Assumes we have a panel of time series where the time index is available.
      The time index is assumed to represent equal spaced periods. At least one
      observation per period is required.
  
      Parameters
      ----------
      results : result instance
         result of a regression, uses results.model.exog and results.resid
         TODO: this should use wexog instead
      nlags : int or None
          Highest lag to include in kernel window. Currently, no default
          because the optimal length will depend on the number of observations
          per cross-sectional unit.
      time : ndarray of int
          this should contain the coding for the time period of each observation.
          time periods should be integers in range(maxT) where maxT is obs of i
      weights_func : callable
          weights_func is called with nlags as argument to get the kernel
          weights. default are Bartlett weights
      use_correction : 'cluster' or 'hac' or False
          If False, then no small sample correction is used.
          If 'hac' (default), then the same correction as in single time series, cov_hac
          is used.
          If 'cluster', then the same correction as in cov_cluster is
          used.
  
      Returns
      -------
      cov : ndarray, (k_vars, k_vars)
          HAC robust covariance matrix for parameter estimates
  
      Notes
      -----
      Tested against STATA xtscc package, which uses no small sample correction
  
      This first averages relevant variables for each time period over all
      individuals/groups, and then applies the same kernel weighted averaging
      over time as in HAC.
  
      Warning:
      In the example with a short panel (few time periods and many individuals)
      with mainly across individual variation this estimator did not produce
      reasonable results.
  
      Options might change when other kernels besides Bartlett and uniform are
      available.
  
      References
      ----------
      Daniel Hoechle, xtscc paper
      Driscoll and Kraay
  
      """
  
      xu, hessian_inv = _get_sandwich_arrays(results)
  
      # S_hac = S_nw_panel(xw, weights, groupidx)
      S_hac = S_hac_groupsum(
          xu, time, nlags=nlags, weights_func=weights_func
      )
      cov_hac = _HCCM2(hessian_inv, S_hac)
      if use_correction:
          nobs, k_params = xu.shape
          if use_correction == "hac":
              cov_hac *= nobs / float(nobs - k_params)
          elif use_correction in ["c", "cluster"]:
              n_groups = len(np.unique(time))
              cov_hac *= n_groups / (n_groups - 1.0)
              cov_hac *= (nobs - 1.0) / float(nobs - k_params)
  
      return cov_hac
  
imports: import numpy
  from statsmodels.tools.grouputils import combine_indices, group_sums
  from statsmodels.stats.moment_helpers import se_cov
label_desc: Use image node_id 23 for calling a global function with example usage: cov_nw_groupsum(results, nlags, time, weights_func, use_correction) and returns: cov_hac