def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.4339072 , -0.08402653, 0.73292344, 1.61661128],
[ 0.05854268, 0.05562941, 0.04034178, 0.0511207 ],
[ 7.4118102 , -1.51046975, 18.16785075, 31.62341666],
[ 0. , 0.1309236 , 0. , 0. ],
[ 0.06713617, 0.05469138, 0.03785006, 0.1071093 ],
[ 0.05504093, 0.0574849 , 0.04350945, 0.02510928]])
cls.res1_conf_int = np.array([[ 0.31916567, 0.54864874],
[-0.19305817, 0.0250051 ],
[ 0.65385501, 0.81199188],
[ 1.51641655, 1.71680602]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.4339072 , -0.08402653, 0.73292344, 1.61661128],
[ 0.05854268, 0.05562941, 0.04034178, 0.0511207 ],
[ 7.4118102 , -1.51046975, 18.16785075, 31.62341666],
[ 0. , 0.1309236 , 0. , 0. ],
[ 0.06713617, 0.05469138, 0.03785006, 0.1071093 ],
[ 0.05504093, 0.0574849 , 0.04350945, 0.02510928]])
cls.res1_conf_int = np.array([[ 0.31916567, 0.54864874],
[-0.19305817, 0.0250051 ],
[ 0.65385501, 0.81199188],
[ 1.51641655, 1.71680602]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
def test_compare_arma():
#this is a preliminary test to compare arma_kf, arma_cond_ls and arma_cond_mle
#the results returned by the fit methods are incomplete
#for now without random.seed
#np.random.seed(9876565)
x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200,
burnin=1000)
# this used kalman filter through descriptive
# d = ARMA(x)
# d.fit((1,1), trend='nc')
# dres = d.res
modkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
reskf = modkf.fit((1,1), trend='nc', disp=-1)
dres = reskf
modc = Arma(x)
resls = modc.fit(order=(1,1))
rescm = modc.fit_mle(order=(1,1), start_params=[0.4,0.4, 1.], disp=0)
#decimal 1 corresponds to threshold of 5% difference
#still different sign corrcted
#assert_almost_equal(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(resls[0] / dres.params, np.ones(dres.params.shape),
decimal=1)
#rescm also contains variance estimate as last element of params
#assert_almost_equal(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(rescm.params[:-1] / dres.params, np.ones(dres.params.shape), decimal=1)
def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = partial(np.random.standard_t, 3)
np.random.seed(8659567)
x = arma_generate_sample(ar,
ma,
nobs,
scale=1,
distrvs=dist,
burnin=500)
with pytest.warns(FutureWarning):
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(
order=order,
start_params=np.r_[cls.res_ls[0], 1],
method="nm",
disp=False,
)
cls.res1_table = np.array([
[0.43390720, -0.08402653, 0.73292344, 1.61661128],
[0.05854268, 0.055629410, 0.04034178, 0.05112070],
[7.41181020, -1.51046975, 18.16785075, 31.62341666],
[0.00000000, 0.130923600, 0.00000000, 0.00000000],
[0.06713617, 0.054691380, 0.03785006, 0.10710930],
[0.05504093, 0.057484900, 0.04350945, 0.02510928],
])
cls.res1_conf_int = np.array([
[0.31916567, 0.54864874],
[-0.19305817, 0.02500510],
[0.65385501, 0.81199188],
[1.51641655, 1.71680602],
])
cls.ls_params = np.array([0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([0.0377741, 0.03567847, 0.02744488])
def test_compare_arma():
#this is a preliminary test to compare arma_kf, arma_cond_ls and arma_cond_mle
#the results returned by the fit methods are incomplete
#for now without random.seed
#np.random.seed(9876565)
x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200,
burnin=1000)
# this used kalman filter through descriptive
# d = ARMA(x)
# d.fit((1,1), trend='nc')
# dres = d.res
modkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
reskf = modkf.fit((1, 1), trend='nc', disp=-1)
dres = reskf
modc = Arma(x)
resls = modc.fit(order=(1, 1))
rescm = modc.fit_mle(order=(1, 1), start_params=[0.4, 0.4, 1.], disp=0)
#decimal 1 corresponds to threshold of 5% difference
#still different sign corrcted
#assert_almost_equal(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(resls[0] / dres.params,
np.ones(dres.params.shape),
decimal=1)
#rescm also contains variance estimate as last element of params
#assert_almost_equal(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(rescm.params[:-1] / dres.params,
np.ones(dres.params.shape),
decimal=1)
from 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])
#res_ols.scale]
#areste.var()]
np.sqrt(res_ols.scale)]
#need to iterate, ar1 too large ma terms too small
#fix large parameters, if hannan_rissannen are too large
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))
#areste.var()]
np.sqrt(res_ols.scale)]
#need to iterate, ar1 too large ma terms too small
#fix large parameters, if hannan_rissannen are too large
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))
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)
print("Arma.fit method = \"CLS\"")
t4=time()
import statsmodels.sandbox.tsa.fftarma as fa from statsmodels.tsa.descriptivestats import TsaDescriptive from statsmodels.tsa.arma_mle import Arma x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) d = TsaDescriptive(x) d.plot4() #d.fit(order=(1,1)) d.fit((1,1), trend='nc') print(d.res.params) modc = Arma(x) resls = modc.fit(order=(1,1)) print(resls[0]) rescm = modc.fit_mle(order=(1,1), start_params=[-0.4,0.4, 1.]) print(rescm.params) #decimal 1 corresponds to threshold of 5% difference assert_almost_equal(resls[0] / d.res.params, 1, decimal=1) assert_almost_equal(rescm.params[:-1] / d.res.params, 1, decimal=1) #copied to tsa.tests plt.figure() plt.plot(x, 'b-o') plt.plot(modc.predicted(), 'r-') plt.figure() plt.plot(modc.error_estimate) #plt.show() from statsmodels.miscmodels.tmodel import TArma
res = mod.fit(order=order)
res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm')
print(res[0])
proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]])
print(ar, ma)
proc.nobs = nobs
# TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__
print(proc.ar, proc.ma)
print(proc.ar_roots(), proc.ma_roots())
from statsmodels.tsa.arma_mle import Arma
modn = Arma(x)
resn = modn.fit_mle(order=order)
moda = ARMA(x, order=order)
resa = moda.fit( trend='nc')
print('\nparameter estimates')
print('ls ', res[0])
print('norm', resn.params)
print('t ', res2.params)
print('A ', resa.params)
print('\nstandard deviation of parameter estimates')
#print 'ls ', res[0] #TODO: not available yet
print('norm', resn.bse)
print('t ', res2.bse)
print('A ', resa.bse)
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)
print("Arma.fit method = \"CLS\"")
t4 = time()
from 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]
