def adf_test(timeseries, trend):
from arch.unitroot import ADF
adf = ADF(timeseries)
adf.trend = str(trend)
reg_res = adf.regression
#print('ADF statistic: {0:0.4f}'.format(adf.stat))
#print('ADF p-value: {0:0.4f}'.format(adf.pvalue))
#print(reg_res.summary().as_text())
return (adf.stat, adf.pvalue)
def test_no_change_lags_trend(self):
adf = ADF(self.inflation)
lags = adf.lags
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.lags = lags
trend = adf.trend
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.trend = trend
ml = adf.max_lags
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.max_lags = ml
def test_adf_auto_t_stat(self):
adf = ADF(self.inflation, method='t-stat')
assert_equal(adf.lags, 10)
old_stat = adf.stat
adf.lags += 1
assert adf.stat != old_stat
old_stat = adf.stat
assert_equal(adf.y, self.inflation)
adf.trend = 'ctt'
assert adf.stat != old_stat
assert adf.trend == 'ctt'
assert len(adf.valid_trends) == len(('nc', 'c', 'ct', 'ctt'))
for d in adf.valid_trends:
assert d in ('nc', 'c', 'ct', 'ctt')
assert adf.null_hypothesis == 'The process contains a unit root.'
assert adf.alternative_hypothesis == 'The process is weakly stationary.'
def test_adf_auto_t_stat(self):
adf = ADF(self.inflation, method='t-stat')
assert_equal(adf.lags, 10)
old_stat = adf.stat
adf.lags += 1
assert adf.stat != old_stat
old_stat = adf.stat
assert_equal(adf.y, self.inflation)
adf.trend = 'ctt'
assert adf.stat != old_stat
assert adf.trend == 'ctt'
assert len(adf.valid_trends) == len(('nc', 'c', 'ct', 'ctt'))
for d in adf.valid_trends:
assert d in ('nc', 'c', 'ct', 'ctt')
assert adf.null_hypothesis == 'The process contains a unit root.'
assert adf.alternative_hypothesis == 'The process is weakly stationary.'
def test_adf_auto_t_stat(self):
adf = ADF(self.inflation, method="t-stat")
assert_equal(adf.lags, 11)
adf2 = ADF(self.inflation, method="t-stat", low_memory=True)
assert_equal(adf2.lags, 11)
old_stat = adf.stat
adf.lags += 1
assert adf.stat != old_stat
old_stat = adf.stat
assert_equal(adf.y, self.inflation)
adf.trend = "ctt"
assert adf.stat != old_stat
assert adf.trend == "ctt"
assert len(adf.valid_trends) == len(("nc", "c", "ct", "ctt"))
for d in adf.valid_trends:
assert d in ("nc", "c", "ct", "ctt")
assert adf.null_hypothesis == "The process contains a unit root."
assert adf.alternative_hypothesis == "The process is weakly " "stationary."
def test_invalid_determinstic(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.trend = 'bad-value'
def test_invalid_determinstic(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.trend = 'bad-value'
table1[t1_cols[0]] = np.mean(JC3).tolist()
table1[t1_cols[1]] = np.std(JC3).tolist()
table1[t1_cols[2]] = np.percentile(JC3, 25, axis=0).tolist()
table1[t1_cols[3]] = np.percentile(JC3, 50, axis=0).tolist()
table1[t1_cols[4]] = np.percentile(JC3, 75, axis=0).tolist()
table1.iloc[1, :] = table1.iloc[1, :] * 1000
table1.iloc[6, :] = table1.iloc[6, :] / 10000
### Table 2
JC1 = pd.read_csv('JC1.csv', header=0)
JC1 = JC1[['ore_price', 'growth', 'VIX', 'BDI']]
JC1_cols = JC1.columns.tolist()
adf_iron = ADF(JC1[JC1_cols[0]])
adf_iron.trend = 'c'
adf_growth = ADF(JC1[JC1_cols[1]])
adf_growth.trend = 'c'
adf_vix = ADF(JC1[JC1_cols[2]])
adf_vix.trend = 'c'
adf_BDI = ADF(JC1[JC1_cols[3]])
adf_BDI.trend = 'c'
adf_stat = [adf_iron.stat, adf_growth.stat, adf_vix.stat, adf_BDI.stat]
adf_p = [adf_iron.pvalue, adf_growth.pvalue, adf_vix.pvalue, adf_BDI.pvalue]
table2 = pd.DataFrame(
index=['ADF-Statistics', 'p-Value', '1%', '5%', '10%'],
