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 __future__ import print_function import numpy as np from numpy.testing import assert_almost_equal import matplotlib.pyplot as plt 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')