code: def cov_nw_panel(
      results,
      nlags,
      groupidx,
      weights_func=weights_bartlett,
      use_correction="hac",
  ):
      """Panel HAC robust covariance matrix
  
      Assumes we have a panel of time series with consecutive, equal spaced time
      periods. Data is assumed to be in long format with time series of each
      individual stacked into one array. Panel can be unbalanced.
  
      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.
      groupidx : list of tuple
          each tuple should contain the start and end index for an individual.
          (groupidx might change in future).
      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 'cluster' (default), then the same correction as in cov_cluster is
          used.
          If 'hac', then the same correction as in single time series, cov_hac
          is used.
  
  
      Returns
      -------
      cov : ndarray, (k_vars, k_vars)
          HAC robust covariance matrix for parameter estimates
  
      Notes
      -----
      For nlags=0, this is just White covariance, cov_white.
      If kernel is uniform, `weights_uniform`, with nlags equal to the number
      of observations per unit in a balance panel, then cov_cluster and
      cov_hac_panel are identical.
  
      Tested against STATA `newey` command with same defaults.
  
      Options might change when other kernels besides Bartlett and uniform are
      available.
  
      """
      if nlags == 0:  # so we can reproduce HC0 White
          weights = [1, 0]  # to avoid the scalar check in hac_nw
      else:
          weights = weights_func(nlags)
  
      xu, hessian_inv = _get_sandwich_arrays(results)
  
      S_hac = S_nw_panel(xu, weights, groupidx)
      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", "clu", "cluster"]:
              n_groups = len(groupidx)
              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 22 for calling a global function with example usage: cov_nw_panel(results, nlags, groupidx, weights_func, use_correction) and returns: cov_hac