def a_test_predict_is_nans():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, q=2, p=2)
x = model.fit()
x.summary()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
def a_test_bbvi_mini_batch_elbo():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('BBVI',
iterations=300,
map_start=False,
mini_batch=32,
record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def a_test_predict_is_intervals_mh():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('M-H', nsims=400)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values +
0.000001 >= predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values +
0.000001 >= predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values +
0.000001 >= predictions['1% Prediction Interval'].values))
def a_test_predict_intervals():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit()
predictions = model.predict(h=10, intervals=True, oos_data=data)
assert (np.all(predictions['99% Prediction Interval'].values +
0.000001 >= predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values +
0.000001 >= predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values +
0.000001 >= predictions['1% Prediction Interval'].values))
def a_test_pml():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('PML')
assert (len(model.latent_variables.z_list) == 8)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def a_test_bbvi_mini_batch():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('BBVI', iterations=100, map_start=False, mini_batch=32)
assert (len(model.latent_variables.z_list) == 8)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def a_test_no_terms():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=0, q=0)
x = model.fit()
assert (len(model.latent_variables.z_list) == 6)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def a_test_ppc():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test_sample_model():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 1)
def a_test_predict_is_nonconstant():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=2, q=2)
x = model.fit()
predictions = model.predict_is(h=5, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def a_test_predict_is_length():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=2, q=2)
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def a_test_predict_length():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=2, q=2)
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data).shape[0] == 5)
def test_bbvi():
model = pf.EGARCHMReg(formula='JPM~Rf', data=data, p=1, q=1)
x = model.fit('BBVI', iterations=100)
assert (len(model.latent_variables.z_list) == 8)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
