class TestVARResultsLutkepohl(object):
"""
Verify calculations using results from Lutkepohl's book
"""
def __init__(self):
self.p = 2
if not have_pandas():
return
sdata, dates = get_lutkepohl_data('e1')
names = sdata.dtype.names
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
self.model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
self.res = self.model.fit(maxlags=self.p)
self.irf = self.res.irf(10)
self.lut = E1_Results()
def test_approx_mse(self):
if not have_pandas():
raise nose.SkipTest
# 3.5.18, p. 99
mse2 = np.array([[25.12, .580, 1.300],
[.580, 1.581, .586],
[1.300, .586, 1.009]]) * 1e-4
assert_almost_equal(mse2, self.res.forecast_cov(3)[1],
DECIMAL_3)
def test_irf_stderr(self):
if not have_pandas():
raise nose.SkipTest
irf_stderr = self.irf.stderr(orth=False)
for i in range(1, 1 + len(self.lut.irf_stderr)):
assert_almost_equal(np.round(irf_stderr[i], 3),
self.lut.irf_stderr[i-1])
def test_cum_irf_stderr(self):
if not have_pandas():
raise nose.SkipTest
stderr = self.irf.cum_effect_stderr(orth=False)
for i in range(1, 1 + len(self.lut.cum_irf_stderr)):
assert_almost_equal(np.round(stderr[i], 3),
self.lut.cum_irf_stderr[i-1])
def test_lr_effect_stderr(self):
if not have_pandas():
raise nose.SkipTest
stderr = self.irf.lr_effect_stderr(orth=False)
orth_stderr = self.irf.lr_effect_stderr(orth=True)
assert_almost_equal(np.round(stderr, 3), self.lut.lr_stderr)
class TestVARResultsLutkepohl(object):
"""
Verify calculations using results from Lutkepohl's book
"""
def __init__(self):
self.p = 2
if not have_pandas():
return
sdata, dates = get_lutkepohl_data('e1')
names = sdata.dtype.names
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
self.model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
self.res = self.model.fit(maxlags=self.p)
self.irf = self.res.irf(10)
self.lut = E1_Results()
def test_approx_mse(self):
if not have_pandas():
raise nose.SkipTest
# 3.5.18, p. 99
mse2 = np.array([[25.12, .580, 1.300],
[.580, 1.581, .586],
[1.300, .586, 1.009]]) * 1e-4
assert_almost_equal(mse2, self.res.forecast_cov(3)[1],
DECIMAL_3)
def test_irf_stderr(self):
if not have_pandas():
raise nose.SkipTest
irf_stderr = self.irf.stderr(orth=False)
for i in range(1, 1 + len(self.lut.irf_stderr)):
assert_almost_equal(np.round(irf_stderr[i], 3),
self.lut.irf_stderr[i-1])
def test_cum_irf_stderr(self):
if not have_pandas():
raise nose.SkipTest
stderr = self.irf.cum_effect_stderr(orth=False)
for i in range(1, 1 + len(self.lut.cum_irf_stderr)):
assert_almost_equal(np.round(stderr[i], 3),
self.lut.cum_irf_stderr[i-1])
def test_lr_effect_stderr(self):
if not have_pandas():
raise nose.SkipTest
stderr = self.irf.lr_effect_stderr(orth=False)
orth_stderr = self.irf.lr_effect_stderr(orth=True)
assert_almost_equal(np.round(stderr, 3), self.lut.lr_stderr)
