print(res5.params)
print(res5.bse)
#fitting once is the same as OLS
#note: I need to create new instance, otherwise it continuous fitting
mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res1 = mod1.fit_iterative(1)
res_ols = mod1._fit_ols()
assert_almost_equal(res1.params, res_ols.params, decimal=12)
assert_almost_equal(res1.bse, res_ols.bse, decimal=13)
#cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced
#panel with full length kernel
#I fixe default correction to be equal
mod2._fit_ols()
cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int))
clubse = se_cov(cov_clu)
cov_uni = sw.cov_nw_panel(mod2.res_pooled,
4,
mod2.group.groupidx,
weights_func=sw.weights_uniform,
use_correction='cluster')
assert_almost_equal(cov_uni, cov_clu, decimal=7)
#without correction
cov_clu2 = sw.cov_cluster(mod2.res_pooled,
dgp.groups.astype(int),
use_correction=False)
cov_uni2 = sw.cov_nw_panel(mod2.res_pooled,
4,
mod2.group.groupidx,
weights_func=sw.weights_uniform,
print res5.params
print res5.bse
# fitting once is the same as OLS
# note: I need to create new instance, otherwise it continuous fitting
mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res1 = mod1.fit_iterative(1)
res_ols = mod1._fit_ols()
assert_almost_equal(res1.params, res_ols.params, decimal=12)
assert_almost_equal(res1.bse, res_ols.bse, decimal=13)
# cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced
# panel with full length kernel
# I fixe default correction to be equal
mod2._fit_ols()
cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int))
clubse = se_cov(cov_clu)
cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform)
assert_almost_equal(cov_uni, cov_clu, decimal=7)
# without correction
cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int), use_correction=False)
cov_uni2 = sw.cov_nw_panel(
mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform, use_correction=False
)
assert_almost_equal(cov_uni2, cov_clu2, decimal=8)
cov_white = sw.cov_white_simple(mod2.res_pooled)
cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx, use_correction="hac")
assert_almost_equal(cov_pnw0, cov_white, decimal=13)
time = np.tile(np.arange(nobs_i), n_groups)
def test_short_panel():
#this checks that some basic statistical properties are satisfied by the
##1lab_results, not verified #1lab_results against other packages
#Note: the ranking of robust bse is different if within=True
#I added within keyword to PanelSample to be able to use old example
#if within is False, then there is no within group variation in exog.
nobs = 100
nobs_i = 5
n_groups = nobs // nobs_i
k_vars = 3
dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma,
corr_args=([1], [1., -0.9],), seed=377769, within=False)
#print 'seed', dgp.seed
y = dgp.generate_panel()
noise = y - dgp.y_true
#test dgp
dgp_cov_e = np.array(
[[ 1. , 0.9 , 0.81 , 0.729 , 0.6561],
[ 0.9 , 1. , 0.9 , 0.81 , 0.729 ],
[ 0.81 , 0.9 , 1. , 0.9 , 0.81 ],
[ 0.729 , 0.81 , 0.9 , 1. , 0.9 ],
[ 0.6561, 0.729 , 0.81 , 0.9 , 1. ]])
npt.assert_almost_equal(dgp.cov, dgp_cov_e, 13)
cov_noise = np.cov(noise.reshape(-1,n_groups, order='F'))
corr_noise = cov2corr(cov_noise)
npt.assert_almost_equal(corr_noise, dgp.cov, 1)
#estimate panel model
mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res2 = mod2.fit_iterative(2)
#whitened residual should be uncorrelated
corr_wresid = np.corrcoef(res2.wresid.reshape(-1,n_groups, order='F'))
assert_maxabs(corr_wresid, np.eye(5), 0.1)
#residual should have same correlation as dgp
corr_resid = np.corrcoef(res2.resid.reshape(-1,n_groups, order='F'))
assert_maxabs(corr_resid, dgp.cov, 0.1)
assert_almost_equal(res2.resid.std(),1, decimal=0)
y_pred = np.dot(mod2.exog, res2.params)
assert_almost_equal(res2.fittedvalues, y_pred, 13)
#compare with OLS
res2_ols = mod2._fit_ols()
npt.assert_(mod2.res_pooled is res2_ols)
res2_ols = mod2.res_pooled #TODO: BUG: requires call to _fit_ols
#fitting once is the same as OLS
#note: I need to create new instance, otherwise it continuous fitting
mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res1 = mod1.fit_iterative(1)
assert_almost_equal(res1.params, res2_ols.params, decimal=13)
assert_almost_equal(res1.bse, res2_ols.bse, decimal=13)
res_ols = OLS(y, dgp.exog).fit()
assert_almost_equal(res1.params, res_ols.params, decimal=13)
assert_almost_equal(res1.bse, res_ols.bse, decimal=13)
#compare with old version
mod_old = ShortPanelGLS2(y, dgp.exog, dgp.groups)
res_old = mod_old.fit()
assert_almost_equal(res2.params, res_old.params, decimal=13)
assert_almost_equal(res2.bse, res_old.bse, decimal=13)
mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res5 = mod5.fit_iterative(5)
#make sure it's different
#npt.assert_array_less(0.009, em.maxabs(res5.bse, res2.bse))
cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int))
clubse = se_cov(cov_clu)
pnwbse = se_cov(sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx))
bser = np.vstack((res2.bse, res5.bse, clubse, pnwbse))
bser_mean = np.mean(bser, axis=0)
#cov_cluster close to robust and PanelGLS
#is up to 24% larger than mean of bser
#npt.assert_array_less(0, clubse / bser_mean - 1)
npt.assert_array_less(clubse / bser_mean - 1, 0.25)
#cov_nw_panel close to robust and PanelGLS
npt.assert_array_less(pnwbse / bser_mean - 1, 0.1)
#OLS underestimates bse, robust at least 60% larger
npt.assert_array_less(0.6, bser_mean / res_ols.bse - 1)
#cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced
#panel with full length kernel
#I fixe default correction to be equal
cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
weights_func=sw.weights_uniform,
use_correction='c')
assert_almost_equal(cov_uni, cov_clu, decimal=13)
#without correction
cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int),
use_correction=False)
cov_uni2 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
weights_func=sw.weights_uniform,
use_correction=False)
assert_almost_equal(cov_uni2, cov_clu2, decimal=13)
cov_white = sw.cov_white_simple(mod2.res_pooled)
cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx,
use_correction='hac')
assert_almost_equal(cov_pnw0, cov_white, decimal=13)
def test_short_panel():
#this checks that some basic statistical properties are satisfied by the
#results, not verified results against other packages
#Note: the ranking of robust bse is different if within=True
#I added within keyword to PanelSample to be able to use old example
#if within is False, then there is no within group variation in exog.
nobs = 100
nobs_i = 5
n_groups = nobs // nobs_i
k_vars = 3
dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma,
corr_args=([1], [1., -0.9],), seed=377769, within=False)
#print 'seed', dgp.seed
y = dgp.generate_panel()
noise = y - dgp.y_true
#test dgp
dgp_cov_e = np.array(
[[ 1. , 0.9 , 0.81 , 0.729 , 0.6561],
[ 0.9 , 1. , 0.9 , 0.81 , 0.729 ],
[ 0.81 , 0.9 , 1. , 0.9 , 0.81 ],
[ 0.729 , 0.81 , 0.9 , 1. , 0.9 ],
[ 0.6561, 0.729 , 0.81 , 0.9 , 1. ]])
npt.assert_almost_equal(dgp.cov, dgp_cov_e, 13)
cov_noise = np.cov(noise.reshape(-1,n_groups, order='F'))
corr_noise = cov2corr(cov_noise)
npt.assert_almost_equal(corr_noise, dgp.cov, 1)
#estimate panel model
mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res2 = mod2.fit_iterative(2)
#whitened residual should be uncorrelated
corr_wresid = np.corrcoef(res2.wresid.reshape(-1,n_groups, order='F'))
assert_maxabs(corr_wresid, np.eye(5), 0.1)
#residual should have same correlation as dgp
corr_resid = np.corrcoef(res2.resid.reshape(-1,n_groups, order='F'))
assert_maxabs(corr_resid, dgp.cov, 0.1)
assert_almost_equal(res2.resid.std(),1, decimal=0)
y_pred = np.dot(mod2.exog, res2.params)
assert_almost_equal(res2.fittedvalues, y_pred, 13)
#compare with OLS
res2_ols = mod2._fit_ols()
npt.assert_(mod2.res_pooled is res2_ols)
res2_ols = mod2.res_pooled #TODO: BUG: requires call to _fit_ols
#fitting once is the same as OLS
#note: I need to create new instance, otherwise it continuous fitting
mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res1 = mod1.fit_iterative(1)
assert_almost_equal(res1.params, res2_ols.params, decimal=13)
assert_almost_equal(res1.bse, res2_ols.bse, decimal=13)
res_ols = OLS(y, dgp.exog).fit()
assert_almost_equal(res1.params, res_ols.params, decimal=13)
assert_almost_equal(res1.bse, res_ols.bse, decimal=13)
#compare with old version
mod_old = ShortPanelGLS2(y, dgp.exog, dgp.groups)
res_old = mod_old.fit()
assert_almost_equal(res2.params, res_old.params, decimal=13)
assert_almost_equal(res2.bse, res_old.bse, decimal=13)
mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups)
res5 = mod5.fit_iterative(5)
#make sure it's different
#npt.assert_array_less(0.009, em.maxabs(res5.bse, res2.bse))
cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int))
clubse = se_cov(cov_clu)
pnwbse = se_cov(sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx))
bser = np.vstack((res2.bse, res5.bse, clubse, pnwbse))
bser_mean = np.mean(bser, axis=0)
#cov_cluster close to robust and PanelGLS
#is up to 24% larger than mean of bser
#npt.assert_array_less(0, clubse / bser_mean - 1)
npt.assert_array_less(clubse / bser_mean - 1, 0.25)
#cov_nw_panel close to robust and PanelGLS
npt.assert_array_less(pnwbse / bser_mean - 1, 0.1)
#OLS underestimates bse, robust at least 60% larger
npt.assert_array_less(0.6, bser_mean / res_ols.bse - 1)
#cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced
#panel with full length kernel
#I fixe default correction to be equal
cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
weights_func=sw.weights_uniform,
use_correction='c')
assert_almost_equal(cov_uni, cov_clu, decimal=13)
#without correction
cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int),
use_correction=False)
cov_uni2 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx,
weights_func=sw.weights_uniform,
use_correction=False)
assert_almost_equal(cov_uni2, cov_clu2, decimal=13)
cov_white = sw.cov_white_simple(mod2.res_pooled)
cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx,
use_correction='hac')
assert_almost_equal(cov_pnw0, cov_white, decimal=13)
