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 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 test_reset_trend():
endog = y_arma[:,0]
mod = ARMA(endog)
res1 = mod.fit(order=(1,1), trend="c", disp=-1)
res2 = mod.fit(order=(1,1), trend="nc", disp=-1)
assert_equal(len(res1.params), len(res2.params)+1)
def test_reset_trend():
endog = y_arma[:, 0]
mod = ARMA(endog)
res1 = mod.fit(order=(1, 1), trend="c", disp=-1)
res2 = mod.fit(order=(1, 1), trend="nc", disp=-1)
assert_equal(len(res1.params), len(res2.params) + 1)
from scikits.statsmodels.miscmodels.tmodel import TArma
modct = TArma(x)
reslst = modc.fit(order=(1,1))
print reslst[0]
rescmt = modct.fit_mle(order=(1,1), start_params=[-0.4,0.4, 10, 1.],maxiter=500,
maxfun=500)
print rescmt.params
from scikits.statsmodels.tsa.arima_model import ARMA
mkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
rkf = mkf.fit((1,1), trend='nc')
print rkf.params
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.,-.85,.35, -0.1],[1,.25,-.7], nsample=1000)
##arma22 = ARMA(y_arma22)
##res22 = arma22.fit(trend = 'nc', order=(2,2))
##print 'kf ',res22.params
##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2))
##print 'css', res22css.params
mod22 = Arma(y_arma22)
resls22 = mod22.fit(order=(2,2))
print 'ls ', resls22[0]
resmle22 = mod22.fit_mle(order=(2,2), maxfun=2000)
print 'mle', resmle22.params
from scikits.statsmodels.miscmodels.tmodel import TArma modct = TArma(x) reslst = modc.fit(order=(1, 1)) print reslst[0] rescmt = modct.fit_mle(order=(1, 1), start_params=[-0.4, 0.4, 10, 1.0], maxiter=500, maxfun=500) print rescmt.params from scikits.statsmodels.tsa.arima_model import ARMA mkf = ARMA(x) ##rkf = mkf.fit((1,1)) ##rkf.params rkf = mkf.fit((1, 1), trend="nc") print rkf.params from scikits.statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) y_arma22 = arma_generate_sample([1.0, -0.85, 0.35, -0.1], [1, 0.25, -0.7], nsample=1000) ##arma22 = ARMA(y_arma22) ##res22 = arma22.fit(trend = 'nc', order=(2,2)) ##print 'kf ',res22.params ##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2)) ##print 'css', res22css.params mod22 = Arma(y_arma22) resls22 = mod22.fit(order=(2, 2)) print "ls ", resls22[0] resmle22 = mod22.fit_mle(order=(2, 2), maxfun=2000)
