def test_OLSsummary_rsquared_label(self):
# Check that the "uncentered" label is correctly added after rsquared
x = [1, 5, 7, 3, 5, 2, 5, 3]
y = [6, 4, 2, 7, 4, 9, 10, 2]
reg_with_constant = OLS(y, x, hasconst=True).fit()
assert 'R-squared:' in str(reg_with_constant.summary2())
assert 'R-squared:' in str(reg_with_constant.summary())
reg_without_constant = OLS(y, x, hasconst=False).fit()
assert 'R-squared (uncentered):' in str(reg_without_constant.summary2())
assert 'R-squared (uncentered):' in str(reg_without_constant.summary())
def test_OLSsummary_rsquared_label(self):
# Check that the "uncentered" label is correctly added after rsquared
x = [1, 5, 7, 3, 5, 2, 5, 3]
y = [6, 4, 2, 7, 4, 9, 10, 2]
reg_with_constant = OLS(y, x, hasconst=True).fit()
assert 'R-squared:' in str(reg_with_constant.summary2())
assert 'R-squared:' in str(reg_with_constant.summary())
reg_without_constant = OLS(y, x, hasconst=False).fit()
assert 'R-squared (uncentered):' in str(
reg_without_constant.summary2())
assert 'R-squared (uncentered):' in str(reg_without_constant.summary())
def test_ols_summary_rsquared_label():
# Check that the "uncentered" label is correctly added after rsquared
x = [1, 5, 7, 3, 5, 2, 5, 3]
y = [6, 4, 2, 7, 4, 9, 10, 2]
reg_with_constant = OLS(y, add_constant(x)).fit()
r2_str = 'R-squared:'
with pytest.warns(UserWarning):
assert r2_str in str(reg_with_constant.summary2())
with pytest.warns(UserWarning):
assert r2_str in str(reg_with_constant.summary())
reg_without_constant = OLS(y, x, hasconst=False).fit()
r2_str = 'R-squared (uncentered):'
with pytest.warns(UserWarning):
assert r2_str in str(reg_without_constant.summary2())
with pytest.warns(UserWarning):
assert r2_str in str(reg_without_constant.summary())
def test_ols_summary_rsquared_label():
# Check that the "uncentered" label is correctly added after rsquared
x = [1, 5, 7, 3, 5, 2, 5, 3]
y = [6, 4, 2, 7, 4, 9, 10, 2]
reg_with_constant = OLS(y, add_constant(x)).fit()
r2_str = 'R-squared:'
with pytest.warns(UserWarning):
assert r2_str in str(reg_with_constant.summary2())
with pytest.warns(UserWarning):
assert r2_str in str(reg_with_constant.summary())
reg_without_constant = OLS(y, x, hasconst=False).fit()
r2_str = 'R-squared (uncentered):'
with pytest.warns(UserWarning):
assert r2_str in str(reg_without_constant.summary2())
with pytest.warns(UserWarning):
assert r2_str in str(reg_without_constant.summary())
result_et_hat_adf = GetADFuller(Y=et_hat, maxlags=1, regression='nc')
print('ADF Statistic: %f' % result_et_hat_adf['adfstat'])
#%%
# Verifying above with statsmodel
sm_result_et_hat_adf = adfuller(et_hat,
maxlag=1,
regression='nc',
autolag=None,
regresults=True)
print(sm_result_et_hat_adf)
resultols = OLS(Y1_t.T, Y2_t_d.T).fit()
resultols.summary2()
#%%
# Plot OLS Fit
# Generate equally spaced X values between the true X range
x_axis = np.linspace(Y2_t.min(), Y2_t.max(), 100)
#Plot the estimated dependent variable
Y1_t_hat = a_hat + beta2_hat * x_axis
# Plot own fit on top of seaborrn scatter + fit
plt.title('Cointegrating Regression: Bitcoin and Ethereum')
ax = sns.regplot(x=Y2_t_Series, y=Y1_t_Series, fit_reg=False)
ax.plot(x_axis, Y1_t_hat, 'r')
plt.legend(['OLS Fit', 'Real Values'], loc='lower right')
print "%0.4f" % my_res_adf['adfstat']
# ===== STABILITY CHECK =====
print key, np.abs(my_res_adf['roots'])
print "passes stability check: {0}".format(is_stable(my_res_adf['roots']))
from statsmodels.regression.linear_model import OLS
Y = y.diff()[1:] # must remove first element from array which is nan
X = pd.concat([x.diff()[1:], e_t_hat.shift(1)[1:]], axis=1)
X_c = add_constant(X)
sm_res_ecm = OLS(Y, X).fit() # fit without constant
sm_res_ecm_c = OLS(Y, X_c).fit() # fit without constant
sm_res_ecm_c.summary2()
sm_res_ecm.summary2()
# ====== FIT TO OU PROCESS ======
# My implementations
from analysis import my_AR # AR(p) model
# Import statsmodels equivalents to validate results
from statsmodels.tsa.ar_model import AR
# Run AR(1) model with constant term with e_t_hat as endogenous variable
my_res_ar = my_AR(endog=e_t_hat, maxlag=1, trend='c')
sm_res_ar = AR(np.array(e_t_hat)).fit(maxlag=3, trend='c', method='cmle')
# Stability Check
