print 'time used:', t1-t0 t1=time() print "CSS MLE - ARMA Class" arma2.fit(order=(2,2), trend='nc', method="css") t2=time() arma2.llf = arma2.loglike_css(arma2._invtransparams(arma2.params)) print "params: ", arma2.params print "sigma: ", arma2.sigma2**.5 results += ["css kalmanf", arma2.params, arma2.sigma2**.5, arma2.llf] print 'time used:', t2-t1 print "Arma.fit_mle results" # have to set nar and nma manually arma1.nar = 2 arma1.nma = 2 t2=time() ret = arma1.fit_mle() t3=time() print "params, first 4, sigma, last 1 ", ret.params results += ["Arma.fit_mle ", ret.params[:4], ret.params[-1], ret.llf] print 'time used:', t3-t2 print "Arma.fit method = \"ls\"" t3=time() ret2 = arma1.fit(order=(2,0,2), method="ls") t4=time() print ret2[0] results += ["Arma.fit ls", ret2[0]] print 'time used:', t4-t3
from scikits.statsmodels.tsa.arma_mle import Arma # local import
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print "\nExample 1"
print '----------'
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar,ma,1000,0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1,1), method='fmin')
print arma1res.params
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print res2.params
print res2.model.hessian(res2.params)
print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)
arest = tsa.arima.ARIMA(y1)
resls = arest.fit((1,0,1))
print resls[0]
print resls[1]
from scikits.statsmodels.tsa.arma_mle import Arma # local import
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print "\nExample 1"
print '----------'
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar, ma, 1000, 0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1, 1), method='fmin')
print arma1res.params
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print res2.params
print res2.model.hessian(res2.params)
print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)
arest = tsa.arima.ARIMA(y1)
resls = arest.fit((1, 0, 1))
print resls[0]
print resls[1]
###bug in current version, fixed in Skipper and 1 more
###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs
###ValueError: array dimensions are not compatible for copy
##arma22 = ARMA_kf(y22, constant=False, order=(2,2))
##res = arma22.fit(start_params=start_params)
##print res.params
print '\nARIMA new'
arest2 = Arma(y22)
naryw = 4 #= 30
resyw = sm.regression.yule_walker(y22, order=naryw, inv=True)
arest2.nar = naryw
arest2.nma = 0
e = arest2.geterrors(np.r_[1, -resyw[0]])
x=sm.tsa.tsatools.lagmat2ds(np.column_stack((y22,e)),3,dropex=1,
trim='both')
yt = x[:,0]
xt = x[:,1:]
res_ols = sm.OLS(yt, xt).fit()
print 'hannan_rissannen'
print res_ols.params
start_params = res_ols.params
start_params_mle = np.r_[-res_ols.params[:2],
res_ols.params[2:],
#res_ols.scale]
#areste.var()]
np.sqrt(res_ols.scale)]
#need to iterate, ar1 too large ma terms too small