def test_smoke(data, trend, lags, leads, common, max_lag, method):
y, x = data
if common:
leads = lags
mod = DynamicOLS(y, x, trend, lags, leads, common, max_lag, max_lag,
method)
mod.fit()
def test_smoke_fit(data, cov_type, kernel, bandwidth, force_int, df_adjust):
y, x = data
mod = DynamicOLS(y, x, "ct", 3, 5, False)
res = mod.fit(cov_type, kernel, bandwidth, force_int, df_adjust)
assert isinstance(res.leads, int)
assert isinstance(res.lags, int)
assert isinstance(res.bandwidth, (int, float))
assert isinstance(res.params, pd.Series)
assert isinstance(res.cov_type, str)
assert isinstance(res.resid, pd.Series)
assert isinstance(res.cov, pd.DataFrame)
assert isinstance(res.kernel, str)
assert isinstance(res.summary().as_text(), str)
assert isinstance(res.summary(True).as_text(), str)
def test_kernels_eviews(trivariate_data, config):
y, x = trivariate_data
kernel = config[0]
ser = float(config[1])
lrvar = config[2]
bw = 9 if kernel == "parzen" else 10
res = DynamicOLS(y, x).fit(kernel=kernel, bandwidth=bw, df_adjust=True)
assert_allclose(res.residual_variance, ser**2.0, rtol=1e-5)
assert_allclose(res.long_run_variance, lrvar, rtol=1e-5)
def test_error(trivariate_data):
y, x = trivariate_data
if isinstance(x, pd.DataFrame):
y = y.iloc[:20]
x = x.iloc[:20]
else:
y = y[:20]
x = x[:20]
with pytest.raises(ValueError, match="max_lag and max_lead are too large"):
DynamicOLS(y, x, max_lag=10, max_lead=10)
def test_auto_eviews(trivariate_data, config):
y, x = trivariate_data
leads, lags = config[0]
expected = np.array(config[1])
params = expected[:, 0]
se = expected[:, 1]
res = DynamicOLS(y, x).fit(bandwidth=1, df_adjust=True)
assert res.leads == leads
assert res.lags == lags
assert_allclose(res.params, params, rtol=1e-4)
assert_allclose(res.std_errors, se, rtol=1e-4)
def test_basic(trivariate_data):
# Tested against Eviews. Note: bandwidth is 1 less than Eviews bandwidth (2)
y, x = trivariate_data
res = DynamicOLS(y, x, leads=1, lags=1).fit(bandwidth=1, df_adjust=True)
assert_allclose(res.params, [-11.65535, 2.301629, 91.65392], rtol=1e-4)
assert_allclose(res.std_errors, [0.102846, 0.017243, 1.341955], rtol=1e-4)
assert_allclose(res.tvalues, [-113.3279, 133.4834, 68.29882], rtol=1e-4)
assert_allclose(res.pvalues, [0.0000, 0.0000, 0.0000], atol=1e-5)
assert_allclose(res.long_run_variance, 39.35663, rtol=1e-4)
assert_allclose(np.sqrt(res.residual_variance), 4.759419, rtol=1e-4)
assert_allclose(res.rsquared, 0.998438, atol=1e-5)
assert_allclose(res.rsquared_adj, 0.998425, atol=1e-5)
def test_direct_eviews(trivariate_data, config):
# Tested against Eviews. Note: bandwidth is 1 less than Eviews bandwidth (2)
y, x = trivariate_data
leads, lags = config[0]
expected = np.array(config[1])
params = expected[:, 0]
se = expected[:, 1]
res = DynamicOLS(y, x, leads=leads, lags=lags).fit(bandwidth=1,
df_adjust=True)
assert res.leads == leads
assert res.lags == lags
assert_allclose(res.params, params, rtol=1e-4)
assert_allclose(res.std_errors, se, rtol=1e-4)
def test_auto_trends_eviews(trivariate_data, config):
y, x = trivariate_data
trend = config[0]
leads, lags = config[1]
expected = np.array(config[2])
params = expected[:, 0]
se = expected[:, 1]
final_resid = config[3]
r2 = config[4]
res = DynamicOLS(y, x, trend=trend).fit(bandwidth=1, df_adjust=True)
assert res.leads == leads
assert res.lags == lags
# Trends not checked since trends intrepreted differently
assert_allclose(res.params.iloc[:2], params[:2], rtol=1e-4)
assert_allclose(res.std_errors[:2], se[:2], rtol=1e-4)
# Check resid to verify equivalent
assert_allclose(res.resid.iloc[-1], final_resid, rtol=1e-4)
assert_allclose(res.rsquared, r2, rtol=1e-5)
def test_invalid_fit_options(data):
y, x = data
with pytest.raises(ValueError, match="kernel is not a "):
DynamicOLS(y, x).fit(kernel="unknown")
with pytest.raises(ValueError, match="Unknown cov_type"):
DynamicOLS(y, x).fit(cov_type="unknown")
def test_invalid_input(data):
y, x = data
with pytest.raises(ValueError, match="method must be one of"):
DynamicOLS(y, x, method="unknown")
with pytest.raises(ValueError, match="Unknown trend. Must be one"):
DynamicOLS(y, x, trend="cttt")
def test_mismatch_lead_lag(data):
y, x = data
with pytest.raises(ValueError, match="common is specified but leads"):
DynamicOLS(y, x, "c", 4, 5, True)
with pytest.raises(ValueError, match="common is specified but max_lead"):
DynamicOLS(y, x, max_lag=6, max_lead=7, common=True)
def test_hac_eviews(trivariate_data, config):
y, x = trivariate_data
res = DynamicOLS(y, x).fit(bandwidth=9, df_adjust=True, cov_type="robust")
expected = np.array(config)
se = expected[:, 1]
assert_allclose(res.std_errors, se, rtol=1e-4)