old_contents: 
  
  import numpy as np
  from numpy.testing import assert_almost_equal
  import scikits.statsmodels.sandbox.tsa.fftarma as fa
  #from scikits.statsmodels.tsa.descriptivestats import TsaDescriptive
  from scikits.statsmodels.tsa.arma_mle import Arma
  from scikits.statsmodels.tsa.arima import ARMA
  
  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)
  
      d = ARMA(x)
      d.fit((1,1), trend='nc')
  
      modc = Arma(x)
      resls = modc.fit(order=(1,1))
      rescm = modc.fit_mle(order=(1,1), start_params=[0.4,0.4, 1.])
  
      #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] / d.params, np.ones(d.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] / d.params, np.ones(d.params.shape), decimal=1)
      #return resls[0], d.params, rescm.params
  
new_contents: 
  
  import numpy as np
  from numpy.testing import assert_almost_equal
  import scikits.statsmodels.sandbox.tsa.fftarma as fa
  #from scikits.statsmodels.tsa.descriptivestats import TsaDescriptive
  from scikits.statsmodels.tsa.arma_mle import Arma
  from scikits.statsmodels.tsa.arima import ARMA
  
  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
  
      from scikits.statsmodels.tsa.arima import ARMA
      modkf = ARMA(x)
      ##rkf = mkf.fit((1,1))
      ##rkf.params
      reskf = modkf.fit((1,1), trend='nc')
      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.])
  
      #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)
      #return resls[0], d.params, rescm.params
  
subject: adjust test to reflect new ARMAResult instance