def unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False):
return adfuller(x,
maxlag=maxlag,
regression=trendorder,
autolag=autolag,
store=store,
regresults=False)
def __init__(self):
self.res1 = adfuller(self.x, regression="nc", autolag=None, maxlag=4)
self.teststat = 3.5227498
self.pvalue = .99999 # Stata does not return a p-value for noconstant.
# Tau^max in MacKinnon (1994) is missing, so it is
# assumed that its right-tail is well-behaved
self.critvalues = [-2.587, -1.950, -1.617]
def __init__(self):
self.res1 = adfuller(self.y, regression="nc", autolag=None,
maxlag=1)
self.teststat = -2.4511596
self.pvalue = 0.013747 # Stata does not return a p-value for noconstant
# this value is just taken from our results
self.critvalues = [-2.587,-1.950,-1.617]
def __init__(self):
self.res1 = adfuller(self.x, regression="nc", autolag=None,
maxlag=4)
self.teststat = 3.5227498
self.pvalue = .99999 # Stata does not return a p-value for noconstant.
# Tau^max in MacKinnon (1994) is missing, so it is
# assumed that its right-tail is well-behaved
self.critvalues = [-2.587, -1.950, -1.617]
def __init__(self):
self.res1 = adfuller(self.y, regression="ct", autolag=None, maxlag=1)
self.teststat = -4.425093
self.pvalue = .00199633
self.critvalues = [-4.006, -3.437, -3.137]
def __init__(self):
self.res1 = adfuller(self.y, regression="c", autolag=None, maxlag=1)
self.teststat = -4.3346988
self.pvalue = .00038661
self.critvalues = [-3.476, -2.883, -2.573]
def __init__(self):
self.res1 = adfuller(self.x, regression="ct", autolag=None, maxlag=4)
self.teststat = -1.8566374
self.pvalue = .67682968
self.critvalues = [-4.007, -3.437, -3.137]
def __init__(self):
self.res1 = adfuller(self.x, regression="c", autolag=None, maxlag=4)
self.teststat = .97505319
self.pvalue = .99399563
self.critvalues = [-3.476, -2.883, -2.573]
def __init__(self):
self.res1 = adfuller(self.y, regression="nc", autolag=None, maxlag=1)
self.teststat = -2.4511596
self.pvalue = 0.013747 # Stata does not return a p-value for noconstant
# this value is just taken from our results
self.critvalues = [-2.587, -1.950, -1.617]
def __init__(self):
self.res1 = adfuller(self.y, regression="ct", autolag=None,
maxlag=1)
self.teststat = -4.425093
self.pvalue = .00199633
self.critvalues = [-4.006, -3.437, -3.137]
def __init__(self):
self.res1 = adfuller(self.y, regression="c", autolag=None,
maxlag=1)
self.teststat = -4.3346988
self.pvalue = .00038661
self.critvalues = [-3.476, -2.883, -2.573]
def __init__(self):
self.res1 = adfuller(self.x, regression="ct", autolag=None,
maxlag=4)
self.teststat = -1.8566374
self.pvalue = .67682968
self.critvalues = [-4.007, -3.437, -3.137]
def __init__(self):
self.res1 = adfuller(self.x, regression="c", autolag=None,
maxlag=4)
self.teststat = .97505319
self.pvalue = .99399563
self.critvalues = [-3.476, -2.883, -2.573]
def unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False):
return adfuller(x, maxlag=maxlag, regression=trendorder, autolag=autolag,
store=store, regresults=False)
regrRet = ols(y=formationRet1, x=formationRet2)
residualsPrc = regrPrc.resid
residualsRet = regrRet.resid
beta = regrPrc.beta[0]
intercept = regrPrc.beta[1]
# if pair[0]=='EWP' and pair[1]=='EWL': #residual JUMPS
# pdb.set_trace()
# print "residuals length = " + str(len(residuals))
# print "running dickey fuller..."
# do dickey fuller to see if residuals are even cointegrated
x = np.array(residualsRet) # residuals of Ret of Prc?
# x = np.array(residualsPrc) #residuals of Ret of Prc?
result = ts.adfuller(x)
pval = result[1]
# print "pval = " + str(pval)
if pval < 0.05:
try:
u = residualsPrc[119] # in terms of price
except:
pdb.set_trace()
# debug
# if (pair[0]=='EWP' and pair[1]=='EWC'):
# print "before setting resids for EWP,EWC"
# pdb.set_trace()