Exemple #1
0
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)
Exemple #2
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def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5):
    '''run Monte Carlo for ARMA(2,2)

    DGP parameters currently hard coded
    also sample size `nsample`

    was not a self contained function, used instances from outer scope
      now corrected

    '''
    #nsample = 1000
    #ar = [1.0, 0, 0]
    if ar is None:
        ar = [1.0, -0.55, -0.1]
    #ma = [1.0, 0, 0]
    if ma is None:
        ma = [1.0, 0.3, 0.2]
    results = []
    results_bse = []
    for _ in range(niter):
        y2 = arma_generate_sample(ar, ma, nsample + 1000, sig)[-nsample:]
        y2 -= y2.mean()
        arest2 = Arma(y2)
        rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2, 2))
        results.append(rhohat2a)
        err2a = arest2.geterrors(rhohat2a)
        sige2a = np.sqrt(np.dot(err2a, err2a) / nsample)
        #print 'sige2a', sige2a,
        #print 'cov_x2a.shape', cov_x2a.shape
        #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
        if not cov_x2a is None:
            results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
        else:
            results_bse.append(np.nan + np.zeros_like(rhohat2a))
    return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
Exemple #3
0
def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5):
    '''run Monte Carlo for ARMA(2,2)

    DGP parameters currently hard coded
    also sample size `nsample`

    was not a self contained function, used instances from outer scope
      now corrected

    '''
    #nsample = 1000
    #ar = [1.0, 0, 0]
    if ar is None:
        ar = [1.0, -0.55, -0.1]
    #ma = [1.0, 0, 0]
    if ma is None:
        ma = [1.0,  0.3,  0.2]
    results = []
    results_bse = []
    for _ in range(niter):
        y2 = arma_generate_sample(ar,ma,nsample+1000, sig)[-nsample:]
        y2 -= y2.mean()
        arest2 = Arma(y2)
        rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,2))
        results.append(rhohat2a)
        err2a = arest2.geterrors(rhohat2a)
        sige2a = np.sqrt(np.dot(err2a,err2a)/nsample)
        #print 'sige2a', sige2a,
        #print 'cov_x2a.shape', cov_x2a.shape
        #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
        if not cov_x2a is None:
            results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
        else:
            results_bse.append(np.nan + np.zeros_like(rhohat2a))
    return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
Exemple #4
0
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)