def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis, nw_lags, nobs, df, nw_overlap):
from pandas.core.frame import group_agg
xx_inv = math.inv(xx)
yv = y.values
if cluster_axis is None:
if nw_lags is None:
return xx_inv * (rmse ** 2)
else:
resid = yv - np.dot(x.values, beta)
m = (x.values.T * resid).T
xeps = math.newey_west(m, nw_lags, nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xeps, xx_inv))
else:
Xb = np.dot(x.values, beta).reshape((len(x.values), 1))
resid = DataFrame(yv[:, None] - Xb, index=y.index, columns=["resid"])
if cluster_axis == 1:
x = x.swaplevel(0, 1).sortlevel(0)
resid = resid.swaplevel(0, 1).sortlevel(0)
m = group_agg(x.values * resid.values, x.index._bounds, lambda x: np.sum(x, axis=0))
if nw_lags is None:
nw_lags = 0
xox = 0
for i in range(len(x.index.levels[0])):
xox += math.newey_west(m[i : i + 1], nw_lags, nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xox, xx_inv))
def test_group_agg():
values = np.ones((10, 2)) * np.arange(10).reshape((10, 1))
bounds = np.arange(5) * 2
f = lambda x: x.mean(axis=0)
agged = group_agg(values, bounds, f)
assert(agged[1][0] == 2.5)
assert(agged[2][0] == 4.5)
def test_group_agg():
values = np.ones((10, 2)) * np.arange(10).reshape((10, 1))
bounds = np.arange(5) * 2
f = lambda x: x.mean(axis=0)
agged = group_agg(values, bounds, f)
assert (agged[1][0] == 2.5)
assert (agged[2][0] == 4.5)
def test_group_agg(self):
values = np.ones((10, 2)) * np.arange(10).reshape((10, 1))
bounds = np.arange(5) * 2
f = lambda x: x.mean(axis=0)
agged = group_agg(values, bounds, f)
assert(agged[1][0] == 2.5)
assert(agged[2][0] == 4.5)
# test a function that doesn't aggregate
f2 = lambda x: np.zeros((2, 2))
self.assertRaises(Exception, group_agg, values, bounds, f2)
def test_group_agg(self):
values = np.ones((10, 2)) * np.arange(10).reshape((10, 1))
bounds = np.arange(5) * 2
f = lambda x: x.mean(axis=0)
agged = group_agg(values, bounds, f)
assert(agged[1][0] == 2.5)
assert(agged[2][0] == 4.5)
# test a function that doesn't aggregate
f2 = lambda x: np.zeros((2,2))
self.assertRaises(Exception, group_agg, values, bounds, f2)
def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis,
nw_lags, nobs, df, nw_overlap):
from pandas.core.frame import group_agg
from pandas.core.panel import LongPanel
xx_inv = math.inv(xx)
if cluster_axis is None:
if nw_lags is None:
return xx_inv * (rmse ** 2)
else:
resid = y.values.squeeze() - np.dot(x.values, beta)
m = (x.values.T * resid).T
xeps = math.newey_west(m, nw_lags, nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xeps, xx_inv))
else:
Xb = np.dot(x.values, beta).reshape((len(x.values), 1))
resid = LongPanel(y.values - Xb, index=y.index,
columns=['resid'])
if cluster_axis == 1:
x = x.swapaxes()
resid = resid.swapaxes()
m = group_agg(x.values * resid.values, x.index._bounds,
lambda x: np.sum(x, axis=0))
if nw_lags is None:
nw_lags = 0
xox = 0
for i in range(len(x.major_axis)):
xox += math.newey_west(m[i : i + 1], nw_lags,
nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xox, xx_inv))
def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis, nw_lags, nobs, df,
nw_overlap):
from pandas.core.frame import group_agg
from pandas.core.panel import LongPanel
xx_inv = math.inv(xx)
yv = y.values
if cluster_axis is None:
if nw_lags is None:
return xx_inv * (rmse**2)
else:
resid = yv - np.dot(x.values, beta)
m = (x.values.T * resid).T
xeps = math.newey_west(m, nw_lags, nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xeps, xx_inv))
else:
Xb = np.dot(x.values, beta).reshape((len(x.values), 1))
resid = LongPanel(yv[:, None] - Xb, index=y.index, columns=['resid'])
if cluster_axis == 1:
x = x.swapaxes()
resid = resid.swapaxes()
m = group_agg(x.values * resid.values, x.index._bounds,
lambda x: np.sum(x, axis=0))
if nw_lags is None:
nw_lags = 0
xox = 0
for i in range(len(x.major_axis)):
xox += math.newey_west(m[i:i + 1], nw_lags, nobs, df, nw_overlap)
return np.dot(xx_inv, np.dot(xox, xx_inv))