def test_select_order(self):
result = self.model.fit(10, ic='aic', verbose=True)
result = self.model.fit(10, ic='fpe', verbose=True)
# bug
model = VAR(self.model.endog)
model.select_order()
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,
freq='Q')
self.res = self.model.fit(maxlags=self.p)
self.irf = self.res.irf(10)
self.lut = E1_Results()
def setupClass(cls):
cls.p = 2
cls.data = get_macrodata()
cls.model = VAR(cls.data)
cls.names = cls.model.names
cls.ref = RResults()
cls.k = len(cls.ref.names)
cls.res = cls.model.fit(maxlags=cls.p)
cls.irf = cls.res.irf(cls.ref.nirfs)
cls.nahead = cls.ref.nahead
cls.fevd = cls.res.fevd()
def test_reorder(self):
#manually reorder
data = self.data.view((float,3))
names = self.names
data2 = np.append(np.append(data[:,2,None], data[:,0,None], axis=1), data[:,1,None], axis=1)
names2 = []
names2.append(names[2])
names2.append(names[0])
names2.append(names[1])
res2 = VAR(data2,names=names2).fit(maxlags=self.p)
#use reorder function
res3 = self.res.reorder(['realinv','realgdp', 'realcons'])
#check if the main results match
assert_almost_equal(res2.params, res3.params)
assert_almost_equal(res2.sigma_u, res3.sigma_u)
assert_almost_equal(res2.bic, res3.bic)
assert_almost_equal(res2.stderr, res3.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,
freq='Q')
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)
def test_names(self):
assert_equal(self.model.names, self.ref.names)
model2 = VAR(self.data, names=self.names)
assert_equal(model2.names, self.ref.names)
def test_constructor(self):
# make sure this works with no names
ndarr = self.data.view((float, 3))
model = VAR(ndarr)
res = model.fit(self.p)
