stats.sandwich_covariance.cov_nw_groupsum()

statsmodels.stats.sandwich_covariance.cov_nw_groupsum

statsmodels.stats.sandwich_covariance.cov_nw_groupsum(results, nlags, time, weights_func=, use_correction=0) [source]

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