def S_crosssection(x, group):
'''inner covariance matrix for White on group sums sandwich
I guess for a single categorical group only,
categorical group, can also be the product/intersection of groups
This is used by cov_cluster and indirectly verified
'''
x_group_sums = group_sums(x, group).T #TODO: why transposed
return S_white_simple(x_group_sums)
def S_crosssection(x, group):
'''inner covariance matrix for White on group sums sandwich
I guess for a single categorical group only,
categorical group, can also be the product/intersection of groups
This is used by cov_cluster and indirectly verified
'''
x_group_sums = group_sums(x, group).T #TODO: why transposed
return S_white_simple(x_group_sums)
def test_group_sums():
# Moved from grouputils __main__ section
g = np.array([0, 0, 1, 2, 1, 1, 2, 0])
group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2), g,
use_bincount=False).T
group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2)[:, :, 0], g)
group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2)[:, :, 1], g)
def S_hac_groupsum(x, time, nlags=None, weights_func=weights_bartlett):
'''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
'''
#needs groupsums
x_group_sums = group_sums(x, time).T #TODO: transpose return in grou_sum
return S_hac_simple(x_group_sums, nlags=nlags, weights_func=weights_func)
def S_hac_groupsum(x, time, nlags=None, weights_func=weights_bartlett):
'''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
'''
#needs groupsums
x_group_sums = group_sums(x, time).T #TODO: transpose return in grou_sum
return S_hac_simple(x_group_sums, nlags=nlags, weights_func=weights_func)