def acovf_explicit(ar, ma, nobs):
"""add correlation of MA representation explicitely
"""
ir = arma_impulse_response(ar, ma)
acovfexpl = [np.dot(ir[: nobs - t], ir[t:nobs]) for t in range(10)]
return acovfexpl
def acovf_explicit(ar, ma, nobs):
'''add correlation of MA representation explicitely
'''
ir = arma_impulse_response(ar, ma)
acovfexpl = [np.dot(ir[:nobs - t], ir[t:nobs]) for t in range(10)]
return acovfexpl
>>> # verified for points [[5,10,20,25]] at 4 decimals with Bhardwaj, Swanson, Journal of Eonometrics 2006
'''
print lpol_fiar(0.4, n=20)
print lpol_fima(-0.4, n=20)
print np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2) #different signs
print np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2) #corrected signs
#test is now in statsmodels.tsa.tests.test_arima_process
from scikits.statsmodels.tsa.tests.test_arima_process import test_fi
test_fi()
ar_true = [1, -0.4]
ma_true = [1, 0.5]
ar_desired = arma_impulse_response(ma_true, ar_true)
ar_app, ma_app, res = ar2arma(ar_desired, 2,1, n=100, mse='ar', start=[0.1])
print ar_app, ma_app
ar_app, ma_app, res = ar2arma(ar_desired, 2,2, n=100, mse='ar', start=[-0.1, 0.1])
print ar_app, ma_app
ar_app, ma_app, res = ar2arma(ar_desired, 2,3, n=100, mse='ar')#, start = [-0.1, 0.1])
print ar_app, ma_app
slow = 1
if slow:
ar_desired = lpol_fiar(0.4, n=100)
ar_app, ma_app, res = ar2arma(ar_desired, 3, 1, n=100, mse='ar')#, start = [-0.1, 0.1])
print ar_app, ma_app
ar_app, ma_app, res = ar2arma(ar_desired, 10, 10, n=100, mse='ar')#, start = [-0.1, 0.1])
print ar_app, ma_app
-0.00231864])
>>> # verified for points [[5,10,20,25]] at 4 decimals with Bhardwaj, Swanson, Journal of Eonometrics 2006
'''
print lpol_fiar(0.4, n=20)
print lpol_fima(-0.4, n=20)
print np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2) #different signs
print np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2) #corrected signs
#test is now in statsmodels.tsa.tests.test_arima_process
from scikits.statsmodels.tsa.tests.test_arima_process import test_fi
test_fi()
ar_true = [1, -0.4]
ma_true = [1, 0.5]
ar_desired = arma_impulse_response(ma_true, ar_true)
ar_app, ma_app, res = ar2arma(ar_desired,
2,
1,
n=100,
mse='ar',
start=[0.1])
print ar_app, ma_app
ar_app, ma_app, res = ar2arma(ar_desired,
2,
2,
n=100,
mse='ar',
start=[-0.1, 0.1])
print ar_app, ma_app
ar_app, ma_app, res = ar2arma(ar_desired, 2, 3, n=100,
def test_arma_impulse_response():
arrep = arma_impulse_response(armarep.ma, armarep.ar, nobs=21)[1:]
marep = arma_impulse_response(armarep.ar, armarep.ma, nobs=21)[1:]
assert_array_almost_equal(armarep.marep.ravel(), marep, 14)
#difference in sign convention to matlab for AR term
assert_array_almost_equal(-armarep.arrep.ravel(), arrep, 14)
def test_fi():
#test identity of ma and ar representation of fi lag polynomial
n = 100
mafromar = arma_impulse_response(lpol_fiar(0.4, n=n), [1], n)
assert_array_almost_equal(mafromar, lpol_fima(0.4, n=n), 13)
from scikits.statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from scikits.statsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
# from movstat import acf, acovf
# from scikits.statsmodels.sandbox.tsa import acf, acovf, pacf
from scikits.statsmodels.tsa.stattools import acf, acovf, pacf
ar = [1.0, -0.6]
# ar = [1., 0.]
ma = [1.0, 0.4]
# ma = [1., 0.4, 0.6]
# ma = [1., 0.]
mod = "" #'ma2'
x = arma_generate_sample(ar, ma, 5000)
x_acf = acf(x)[:10]
x_ir = arma_impulse_response(ar, ma)
# print x_acf[:10]
# print x_ir[:10]
# irc2 = np.correlate(x_ir,x_ir,'full')[len(x_ir)-1:]
# print irc2[:10]
# print irc2[:10]/irc2[0]
# print irc2[:10-1] / irc2[1:10]
# print x_acf[:10-1] / x_acf[1:10]
# detrend helper from matplotlib.mlab
def detrend(x, key=None):
if key is None or key == "constant":
return detrend_mean(x)
elif key == "linear":
return detrend_linear(x)
def test_arma_impulse_response():
arrep = arma_impulse_response(armarep.ma, armarep.ar, nobs=21)[1:]
marep = arma_impulse_response(armarep.ar, armarep.ma, nobs=21)[1:]
assert_array_almost_equal(armarep.marep.ravel(), marep, 14)
#difference in sign convention to matlab for AR term
assert_array_almost_equal(-armarep.arrep.ravel(), arrep, 14)
def test_fi():
#test identity of ma and ar representation of fi lag polynomial
n = 100
mafromar = arma_impulse_response(lpol_fiar(0.4, n=n), [1], n)
assert_array_almost_equal(mafromar, lpol_fima(0.4, n=n), 13)
from scikits.statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from scikits.statsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
#from movstat import acf, acovf
#from scikits.statsmodels.sandbox.tsa import acf, acovf, pacf
from scikits.statsmodels.tsa.stattools import acf, acovf, pacf
ar = [1., -0.6]
#ar = [1., 0.]
ma = [1., 0.4]
#ma = [1., 0.4, 0.6]
#ma = [1., 0.]
mod = '' #'ma2'
x = arma_generate_sample(ar, ma, 5000)
x_acf = acf(x)[:10]
x_ir = arma_impulse_response(ar, ma)
#print x_acf[:10]
#print x_ir[:10]
#irc2 = np.correlate(x_ir,x_ir,'full')[len(x_ir)-1:]
#print irc2[:10]
#print irc2[:10]/irc2[0]
#print irc2[:10-1] / irc2[1:10]
#print x_acf[:10-1] / x_acf[1:10]
# detrend helper from matplotlib.mlab
def detrend(x, key=None):
if key is None or key == 'constant':
return detrend_mean(x)
elif key == 'linear':