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.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]
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()