3.11.19.4.2. statsmodels.stats.sandwich_covariance.S_hac_groupsum¶

statsmodels.stats.sandwich_covariance.S_hac_groupsum(x, time, nlags=None, weights_func=<function weights_bartlett>)[source]¶

inner covariance matrix for HAC over group sums sandwich

This assumes we have complete equal spaced time periods. The number of time periods per group need not be the same, but we need at least one observation for each time period

For a single categorical group only, or a everything else but time dimension. This first aggregates x over groups for each time period, then applies HAC on the sum per period.

Parameters:

x : ndarray (nobs,) or (nobs, k_var)

data, for HAC this is array of x_i * u_i

time : ndarray, (nobs,)

timeindes, assumed to be integers range(n_periods)

nlags : int or None

highest lag to include in kernel window. If None, then nlags = floor[4(T/100)^(2/9)] is used.

weights_func : callable

weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights

Returns:

S : ndarray, (k_vars, k_vars)

inner covariance matrix for sandwich

References

Daniel Hoechle, xtscc paper Driscoll and Kraay