kwargs.setdefault('marker', 'o')
kwargs.setdefault('linestyle', 'None')
d = self.plot(lags, c, **kwargs)
else:
kwargs.setdefault('marker', 'o')
kwargs.setdefault('linestyle', 'None')
a, = self.plot(lags, c, **kwargs)
b = None
return lags, c, a, b
arrvs = ar_generator()
##arma = ARIMA()
##res = arma.fit(arrvs[0], 4, 0)
arma = ARIMA(arrvs[0])
res = arma.fit((4, 0, 0))
print res[0]
acf1 = acf(arrvs[0])
acovf1b = acovf(arrvs[0], unbiased=False)
acf2 = autocorr(arrvs[0])
acf2m = autocorr(arrvs[0] - arrvs[0].mean())
print acf1[:10]
print acovf1b[:10]
print acf2[:10]
print acf2m[:10]
x = arma_generate_sample([1.0, -0.8], [1.0], 500)
print acf(x)[:20]
kwargs.setdefault("marker", "o")
kwargs.setdefault("linestyle", "None")
d = self.plot(lags, c, **kwargs)
else:
kwargs.setdefault("marker", "o")
kwargs.setdefault("linestyle", "None")
a, = self.plot(lags, c, **kwargs)
b = None
return lags, c, a, b
arrvs = ar_generator()
##arma = ARIMA()
##res = arma.fit(arrvs[0], 4, 0)
arma = ARIMA(arrvs[0])
res = arma.fit((4, 0, 0))
print res[0]
acf1 = acf(arrvs[0])
acovf1b = acovf(arrvs[0], unbiased=False)
acf2 = autocorr(arrvs[0])
acf2m = autocorr(arrvs[0] - arrvs[0].mean())
print acf1[:10]
print acovf1b[:10]
print acf2[:10]
print acf2m[:10]
x = arma_generate_sample([1.0, -0.8], [1.0], 500)
start_params_mle[:-1] = (np.sign(start_params_mle[:-1])
* np.minimum(np.abs(start_params_mle[:-1]),0.75))
print 'conditional least-squares'
#print rhohat2
print 'with mle'
arest2.nar = 2
arest2.nma = 2
#
res = arest2.fit_mle(start_params=start_params_mle, method='nm') #no order in fit
print res.params
rhohat2, cov_x2a, infodict, mesg, ier = arest2.fit((2,2))
print '\nARIMA_old'
arest = ARIMA_old(y22)
rhohat1, cov_x1, infodict, mesg, ier = arest.fit((2,0,2))
print rhohat1
print np.sqrt(np.diag(cov_x1))
err1 = arest.errfn(x=y22)
print np.var(err1)
print 'bse ls, formula not checked'
print np.sqrt(np.diag(cov_x1))*err1.std()
print 'bsejac for mle'
#print arest2.bsejac
#TODO:check bsejac raises singular matrix linalg error
#in model.py line620: return np.linalg.inv(np.dot(jacv.T, jacv))
print '\nyule-walker'
print sm.regression.yule_walker(y22, order=2, inv=True)
