def test_harx(self):
harx = HARX(self.y, self.x, lags=[1, 5, 22])
params = np.array([1.0, 0.4, 0.3, 0.2, 1.0, 1.0])
data = harx.simulate(params, self.T, x=randn(self.T + 500, 1))
iv = randn(22, 1)
data = harx.simulate(params,
self.T,
x=randn(self.T + 500, 1),
initial_value=iv)
assert_equal(data.shape, (self.T, 3))
cols = ['data', 'volatility', 'errors']
for c in cols:
assert_true(c in data)
bounds = harx.bounds()
for b in bounds:
assert_equal(b[0], -np.inf)
assert_equal(b[1], np.inf)
assert_equal(len(bounds), 5)
assert_equal(harx.num_params, 1 + 3 + self.x.shape[1])
assert_equal(harx.constant, True)
a, b = harx.constraints()
assert_equal(a, np.empty((0, 5)))
assert_equal(b, np.empty(0))
res = harx.fit()
assert_raises(RuntimeError, res.forecast, horizon=10)
assert_raises(ValueError, res.forecast, params=np.array([1.0, 1.0]))
nobs = self.T - 22
rhs = np.ones((nobs, 5))
y = self.y
lhs = y[22:]
for i in range(self.T - 22):
rhs[i, 1] = y[i + 21]
rhs[i, 2] = np.mean(y[i + 17:i + 22])
rhs[i, 3] = np.mean(y[i:i + 22])
rhs[:, 4] = self.x[22:, 0]
params = np.linalg.pinv(rhs).dot(lhs)
assert_almost_equal(params, res.params[:-1])
assert_equal(harx.first_obs, 22)
assert_equal(harx.last_obs, 1000)
assert_equal(harx.hold_back, None)
assert_equal(harx.lags, [1, 5, 22])
assert_equal(harx.nobs, self.T - 22)
assert_equal(harx.name, 'HAR-X')
assert_equal(harx.use_rotated, False)
harx
harx._repr_html_()
res = harx.fit(cov_type='mle')
res
def test_harx(self):
harx = HARX(self.y, self.x, lags=[1, 5, 22])
assert harx.x is self.x
params = np.array([1.0, 0.4, 0.3, 0.2, 1.0, 1.0])
harx.simulate(params, self.T, x=self.rng.randn(self.T + 500, 1))
iv = self.rng.randn(22, 1)
data = harx.simulate(params,
self.T,
x=self.rng.randn(self.T + 500, 1),
initial_value=iv)
assert_equal(data.shape, (self.T, 3))
cols = ["data", "volatility", "errors"]
for c in cols:
assert c in data
bounds = harx.bounds()
for b in bounds:
assert_equal(b[0], -np.inf)
assert_equal(b[1], np.inf)
assert_equal(len(bounds), 5)
assert_equal(harx.num_params, 1 + 3 + self.x.shape[1])
assert_equal(harx.constant, True)
a, b = harx.constraints()
assert_equal(a, np.empty((0, 5)))
assert_equal(b, np.empty(0))
res = harx.fit(disp=DISPLAY)
with pytest.raises(ValueError):
res.forecast(params=np.array([1.0, 1.0]))
nobs = self.T - 22
rhs = np.ones((nobs, 5))
y = self.y
lhs = y[22:]
for i in range(self.T - 22):
rhs[i, 1] = y[i + 21]
rhs[i, 2] = np.mean(y[i + 17:i + 22])
rhs[i, 3] = np.mean(y[i:i + 22])
rhs[:, 4] = self.x[22:, 0]
params = np.linalg.pinv(rhs).dot(lhs)
assert_almost_equal(params, res.params[:-1])
assert harx.hold_back is None
assert_equal(harx.lags, [1, 5, 22])
assert_equal(harx.name, "HAR-X")
assert_equal(harx.use_rotated, False)
assert isinstance(harx.__repr__(), str)
harx._repr_html_()
res = harx.fit(cov_type="classic", disp=DISPLAY)
assert isinstance(res.__repr__(), str)
def test_harx(self):
harx = HARX(self.y, self.x, lags=[1, 5, 22])
params = np.array([1.0, 0.4, 0.3, 0.2, 1.0, 1.0])
data = harx.simulate(params, self.T, x=randn(self.T + 500, 1))
iv = randn(22, 1)
data = harx.simulate(params, self.T, x=randn(self.T + 500, 1),
initial_value=iv)
assert_equal(data.shape, (self.T, 3))
cols = ['data', 'volatility', 'errors']
for c in cols:
assert_true(c in data)
bounds = harx.bounds()
for b in bounds:
assert_equal(b[0], -np.inf)
assert_equal(b[1], np.inf)
assert_equal(len(bounds), 5)
assert_equal(harx.num_params, 1 + 3 + self.x.shape[1])
assert_equal(harx.constant, True)
a, b = harx.constraints()
assert_equal(a, np.empty((0, 5)))
assert_equal(b, np.empty(0))
res = harx.fit()
assert_raises(RuntimeError, res.forecast, horizon=10)
assert_raises(ValueError, res.forecast, params=np.array([1.0, 1.0]))
nobs = self.T - 22
rhs = np.ones((nobs, 5))
y = self.y
lhs = y[22:]
for i in range(self.T - 22):
rhs[i, 1] = y[i + 21]
rhs[i, 2] = np.mean(y[i + 17:i + 22])
rhs[i, 3] = np.mean(y[i:i + 22])
rhs[:, 4] = self.x[22:, 0]
params = np.linalg.pinv(rhs).dot(lhs)
assert_almost_equal(params, res.params[:-1])
assert_equal(harx.first_obs, 22)
assert_equal(harx.last_obs, 1000)
assert_equal(harx.hold_back, None)
assert_equal(harx.lags, [1, 5, 22])
assert_equal(harx.nobs, self.T - 22)
assert_equal(harx.name, 'HAR-X')
assert_equal(harx.use_rotated, False)
harx
harx._repr_html_()
res = harx.fit(cov_type='mle')
res
def test_har(self):
har = HARX(self.y, lags=[1, 5, 22])
params = np.array([1.0, 0.4, 0.3, 0.2, 1.0])
data = har.simulate(params, self.T)
assert_equal(data.shape, (self.T, 3))
cols = ['data', 'volatility', 'errors']
for c in cols:
assert c in data
bounds = har.bounds()
for b in bounds:
assert_equal(b[0], -np.inf)
assert_equal(b[1], np.inf)
assert_equal(len(bounds), 4)
assert_equal(har.num_params, 4)
assert_equal(har.constant, True)
a, b = har.constraints()
assert_equal(a, np.empty((0, 4)))
assert_equal(b, np.empty(0))
res = har.fit(disp=DISPLAY)
nobs = self.T - 22
rhs = np.ones((nobs, 4))
y = self.y
lhs = y[22:]
for i in range(self.T - 22):
rhs[i, 1] = y[i + 21]
rhs[i, 2] = np.mean(y[i + 17:i + 22])
rhs[i, 3] = np.mean(y[i:i + 22])
params = np.linalg.pinv(rhs).dot(lhs)
assert_almost_equal(params, res.params[:-1])
with pytest.raises(ValueError):
res.forecast(horizon=6, start=0)
forecasts = res.forecast(horizon=6)
t = self.y.shape[0]
direct = pd.DataFrame(index=np.arange(t),
columns=['h.' + str(i + 1) for i in range(6)],
dtype=np.float64)
params = np.asarray(res.params)
fcast = np.zeros(t + 6)
for i in range(21, t):
fcast[:i + 1] = self.y[:i + 1]
fcast[i + 1:] = 0.0
for h in range(6):
fcast[i + h + 1] = params[0]
fcast[i + h + 1] += params[1] * fcast[i + h:i + h + 1]
fcast[i + h + 1] += params[2] * fcast[
i + h - 4:i + h + 1].mean()
fcast[i + h + 1] += params[3] * fcast[
i + h - 21:i + h + 1].mean()
direct.iloc[i, :] = fcast[i + 1:i + 7]
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
forecasts = res.forecast(res.params, horizon=6)
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
assert_equal(har.hold_back, None)
assert_equal(har.lags, [1, 5, 22])
assert_equal(har.name, 'HAR')
assert_equal(har.use_rotated, False)
har = HARX(self.y_series, lags=[1, 5, 22])
res = har.fit(disp=DISPLAY)
direct = pd.DataFrame(index=self.y_series.index,
columns=['h.' + str(i + 1) for i in range(6)],
dtype=np.float64)
forecasts = res.forecast(horizon=6)
params = np.asarray(res.params)
fcast = np.zeros(t + 6)
for i in range(21, t):
fcast[:i + 1] = self.y[:i + 1]
fcast[i + 1:] = 0.0
for h in range(6):
fcast[i + h + 1] = params[0]
fcast[i + h + 1] += params[1] * fcast[i + h:i + h + 1]
fcast[i + h + 1] += params[2] * fcast[
i + h - 4:i + h + 1].mean()
fcast[i + h + 1] += params[3] * fcast[
i + h - 21:i + h + 1].mean()
direct.iloc[i, :] = fcast[i + 1:i + 7]
assert isinstance(forecasts, ARCHModelForecast)
def test_har(self):
har = HARX(self.y, lags=[1, 5, 22])
params = np.array([1.0, 0.4, 0.3, 0.2, 1.0])
data = har.simulate(params, self.T)
assert_equal(data.shape, (self.T, 3))
cols = ['data', 'volatility', 'errors']
for c in cols:
assert c in data
bounds = har.bounds()
for b in bounds:
assert_equal(b[0], -np.inf)
assert_equal(b[1], np.inf)
assert_equal(len(bounds), 4)
assert_equal(har.num_params, 4)
assert_equal(har.constant, True)
a, b = har.constraints()
assert_equal(a, np.empty((0, 4)))
assert_equal(b, np.empty(0))
res = har.fit(disp=DISPLAY)
nobs = self.T - 22
rhs = np.ones((nobs, 4))
y = self.y
lhs = y[22:]
for i in range(self.T - 22):
rhs[i, 1] = y[i + 21]
rhs[i, 2] = np.mean(y[i + 17:i + 22])
rhs[i, 3] = np.mean(y[i:i + 22])
params = np.linalg.pinv(rhs).dot(lhs)
assert_almost_equal(params, res.params[:-1])
with pytest.raises(ValueError):
res.forecast(horizon=6, start=0)
forecasts = res.forecast(horizon=6)
t = self.y.shape[0]
direct = pd.DataFrame(index=np.arange(t),
columns=['h.' + str(i + 1) for i in range(6)],
dtype=np.float64)
params = np.asarray(res.params)
fcast = np.zeros(t + 6)
for i in range(21, t):
fcast[:i + 1] = self.y[:i + 1]
fcast[i + 1:] = 0.0
for h in range(6):
fcast[i + h + 1] = params[0]
fcast[i + h + 1] += params[1] * fcast[i + h:i + h + 1]
fcast[i + h + 1] += params[2] * fcast[
i + h - 4:i + h + 1].mean()
fcast[i + h + 1] += params[3] * fcast[
i + h - 21:i + h + 1].mean()
direct.iloc[i, :] = fcast[i + 1:i + 7]
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
forecasts = res.forecast(res.params, horizon=6)
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
assert_equal(har.hold_back, None)
assert_equal(har.lags, [1, 5, 22])
assert_equal(har.name, 'HAR')
assert_equal(har.use_rotated, False)
har = HARX(self.y_series, lags=[1, 5, 22])
res = har.fit(disp=DISPLAY)
direct = pd.DataFrame(index=self.y_series.index,
columns=['h.' + str(i + 1) for i in range(6)],
dtype=np.float64)
forecasts = res.forecast(horizon=6)
params = np.asarray(res.params)
fcast = np.zeros(t + 6)
for i in range(21, t):
fcast[:i + 1] = self.y[:i + 1]
fcast[i + 1:] = 0.0
for h in range(6):
fcast[i + h + 1] = params[0]
fcast[i + h + 1] += params[1] * fcast[i + h:i + h + 1]
fcast[i + h + 1] += params[2] * fcast[
i + h - 4:i + h + 1].mean()
fcast[i + h + 1] += params[3] * fcast[
i + h - 21:i + h + 1].mean()
direct.iloc[i, :] = fcast[i + 1:i + 7]
assert isinstance(forecasts, ARCHModelForecast)