def test_dfgls_c(self):
dfgls = DFGLS(self.inflation, trend="c", lags=0)
assert_almost_equal(dfgls.stat, -6.017304, DECIMAL_4)
dfgls.summary()
dfgls.regression.summary()
assert dfgls.trend == "c"
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.trend = "ct"
assert dfgls.trend == "ct"
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.trend = "c"
assert dfgls.trend == "c"
def DickeyFullerGlsTest(data, printResults=True, trend=None, lags=None):
options_Trend = trend if trend != None else {'c','ct'}
options_Lags = lags if lags != None else {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}
#options_LagMethod = lagMethod if lagMethod != None else {'AIC', 'BIC', 't-stat', None}
results = dict()
for column in data.columns:
print("Dickey Fuller GLS test for column: " + column)
results_Trend = dict()
for option_Trend in options_Trend:
results_Lag = dict()
for option_Lag in options_Lags:
result = DFGLS(data[column].dropna(), trend=option_Trend, lags=option_Lag)
if printResults:
result.summary()
results_Lag[option_Lag] = result
results_Trend[option_Trend] = results_Lag
results[column] = results_Trend
return results
def test_dfgls_c(self):
dfgls = DFGLS(self.inflation, trend="c", lags=0)
assert_almost_equal(dfgls.stat, -6.017304, DECIMAL_4)
dfgls.summary()
dfgls.regression.summary()
assert dfgls.trend == "c"
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.trend = "c"
assert dfgls.trend == "c"
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.trend = "ct"
assert dfgls.trend == "ct"
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.trend = "c"
assert dfgls.trend == "c"
dfgls_hm = DFGLS(self.inflation, trend="c", lags=0, low_memory=False)
assert_almost_equal(dfgls_hm.stat, -6.017304, DECIMAL_4)
dfgls_lm = DFGLS(self.inflation, trend="c", lags=0, low_memory=True)
assert_almost_equal(dfgls_lm.stat, -6.017304, DECIMAL_4)
ml = dfgls.max_lags
with pytest.warns(FutureWarning, match="Mutating unit root"):
dfgls.max_lags = ml
def test_dfgls(self):
dfgls = DFGLS(self.inflation, trend='ct', lags=0)
assert_almost_equal(dfgls.stat, -6.300927, DECIMAL_4)
dfgls.summary()
dfgls.regression.summary()
"""
#needs to have as input the prices or the residuals or the difference of the logs of the prices (i.e. log returns)
#arch: DFGLS: if statistic is less than critical value reject null_H=rw
from arch.unitroot import DFGLS
arr = np.array(df1['spread'].values)
dfgls = DFGLS(
arr[-30:]
) #105 is the least data needed to calculate simple regression beta; 58 is the mninimum for overall accuracy of the model
dfgls.trend = 'ct' #Constant and Linear Time Trend
#dfgls.trend = 'c' #Constant trend
print(
'If the dfgls test statistic is less than the critical value and pvalue=near zero, the series is mean reverting:'
)
print(dfgls.summary().as_text())
#needs to have as input the log returns of the prices or the difference of the residuals
#or difference of the log (p) -log (p.shift(1))
from vratio_hu import LoMac
df1["spread_DIFF"] = df1["spread"] - df1["spread"].shift(1)
df1 = df1.dropna()
arr = np.array(df1["spread_DIFF"][-300:].values)
l_k = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
arr_k = np.array(l_k)
print(
"If series is a rw, VR = 1, M1 = N(0,1) for iid series, M2 = N(0,1) for non iid series i.e. M1 = 0, M2 = 0"
)
print(
"If VR != 1, e.g. .4 (<1) and abs(M1) or abs(M2) greater than 1.96 for 2-tailed @ 95% cert, series is probably mean reverting"
def test_dfgls(self):
dfgls = DFGLS(self.inflation, trend='ct', lags=0)
assert_almost_equal(dfgls.stat, -6.300927, DECIMAL_4)
dfgls.summary()
dfgls.regression.summary()
def test_dfgls_c(self):
dfgls = DFGLS(self.inflation, trend="c", lags=0)
assert_almost_equal(dfgls.stat, -6.017304, DECIMAL_4)
dfgls.summary()
dfgls.regression.summary()
