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)
def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.4339072 , -0.08402653, 0.73292344, 1.61661128],
[ 0.05854268, 0.05562941, 0.04034178, 0.0511207 ],
[ 7.4118102 , -1.51046975, 18.16785075, 31.62341666],
[ 0. , 0.1309236 , 0. , 0. ],
[ 0.06713617, 0.05469138, 0.03785006, 0.1071093 ],
[ 0.05504093, 0.0574849 , 0.04350945, 0.02510928]])
cls.res1_conf_int = np.array([[ 0.31916567, 0.54864874],
[-0.19305817, 0.0250051 ],
[ 0.65385501, 0.81199188],
[ 1.51641655, 1.71680602]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.4339072 , -0.08402653, 0.73292344, 1.61661128],
[ 0.05854268, 0.05562941, 0.04034178, 0.0511207 ],
[ 7.4118102 , -1.51046975, 18.16785075, 31.62341666],
[ 0. , 0.1309236 , 0. , 0. ],
[ 0.06713617, 0.05469138, 0.03785006, 0.1071093 ],
[ 0.05504093, 0.0574849 , 0.04350945, 0.02510928]])
cls.res1_conf_int = np.array([[ 0.31916567, 0.54864874],
[-0.19305817, 0.0250051 ],
[ 0.65385501, 0.81199188],
[ 1.51641655, 1.71680602]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
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 cov_x2a is not 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)
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 setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = partial(np.random.standard_t, 3)
np.random.seed(8659567)
x = arma_generate_sample(ar,
ma,
nobs,
scale=1,
distrvs=dist,
burnin=500)
with pytest.warns(FutureWarning):
mod = Arma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(
order=order,
start_params=np.r_[cls.res_ls[0], 1],
method="nm",
disp=False,
)
cls.res1_table = np.array([
[0.43390720, -0.08402653, 0.73292344, 1.61661128],
[0.05854268, 0.055629410, 0.04034178, 0.05112070],
[7.41181020, -1.51046975, 18.16785075, 31.62341666],
[0.00000000, 0.130923600, 0.00000000, 0.00000000],
[0.06713617, 0.054691380, 0.03785006, 0.10710930],
[0.05504093, 0.057484900, 0.04350945, 0.02510928],
])
cls.res1_conf_int = np.array([
[0.31916567, 0.54864874],
[-0.19305817, 0.02500510],
[0.65385501, 0.81199188],
[1.51641655, 1.71680602],
])
cls.ls_params = np.array([0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([0.0377741, 0.03567847, 0.02744488])
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)
import statsmodels.api as sm
from statsmodels.sandbox import tsa
from statsmodels.tsa.arma_mle import Arma # local import
from statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print("\nExample 1")
print('----------')
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar, ma, 1000, 0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1, 1), method='fmin')
print(arma1res.params)
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print(res2.params)
print(res2.model.hessian(res2.params))
print(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))
arest = tsa.arima.ARIMA(y1)
resls = arest.fit((1, 0, 1))
y22 = arma_generate_sample(ar, ma, nobs + 1000, 0.5)[-nobs:]
y22 -= y22.mean()
start_params = [0.1, 0.1, 0.1, 0.1]
start_params_lhs = [-0.1, -0.1, 0.1, 0.1]
print 'truelhs', np.r_[ar[1:], ma[1:]]
###bug in current version, fixed in Skipper and 1 more
###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs
###ValueError: array dimensions are not compatible for copy
##arma22 = ARMA_kf(y22, constant=False, order=(2,2))
##res = arma22.fit(start_params=start_params)
##print res.params
print '\nARIMA new'
arest2 = Arma(y22)
naryw = 4 #= 30
resyw = sm.regression.yule_walker(y22, order=naryw, inv=True)
arest2.nar = naryw
arest2.nma = 0
e = arest2.geterrors(np.r_[1, -resyw[0]])
x = sm.tsa.tsatools.lagmat2ds(np.column_stack((y22, e)),
3,
dropex=1,
trim='both')
yt = x[:, 0]
xt = x[:, 1:]
res_ols = sm.OLS(yt, xt).fit()
print 'hannan_rissannen'
print res_ols.params
print('truelhs', np.r_[ar[1:], ma[1:]])
###bug in current version, fixed in Skipper and 1 more
###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs
###ValueError: array dimensions are not compatible for copy
##from statsmodels.tsa.arima import ARMA as ARMA_kf
##arma22 = ARMA_kf(y22, constant=False, order=(2,2))
##res = arma22.fit(start_params=start_params)
##print res.params
print('\nARIMA new')
arest2 = Arma(y22)
naryw = 4 #= 30
resyw = sm.regression.yule_walker(y22, order=naryw, inv=True)
arest2.nar = naryw
arest2.nma = 0
e = arest2.geterrors(np.r_[1, -resyw[0]])
x=sm.tsa.tsatools.lagmat2ds(np.column_stack((y22,e)),3,dropex=1,
trim='both')
yt = x[:,0]
xt = x[:,1:]
res_ols = sm.OLS(yt, xt).fit()
print('hannan_rissannen')
print(res_ols.params)
start_params = res_ols.params
start_params_mle = np.r_[-res_ols.params[:2],
from __future__ import print_function
from time import time
from statsmodels.tsa.arma_mle import Arma
from statsmodels.tsa.api import ARMA
import numpy as np
print("Battle of the dueling ARMAs")
y_arma22 = np.loadtxt(r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt')
arma1 = Arma(y_arma22)
arma2 = ARMA(y_arma22)
print("The actual results from gretl exact mle are")
params_mle = np.array([.826990, -.333986, .0362419, -.792825])
sigma_mle = 1.094011
llf_mle = -1510.233
print("params: ", params_mle)
print("sigma: ", sigma_mle)
print("llf: ", llf_mle)
print("The actual results from gretl css are")
params_css = np.array([.824810, -.337077, .0407222, -.789792])
sigma_css = 1.095688
llf_css = -1507.301
results = []
results += ["gretl exact mle", params_mle, sigma_mle, llf_mle]
results += ["gretl css", params_css, sigma_css, llf_css]
t0 = time()
print("Exact MLE - Kalman filter version using l_bfgs_b")
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') plt.plot(modc.predicted(), 'r-') plt.figure() plt.plot(modc.error_estimate)
order = (2, 1)
res = mod.fit(order=order)
res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm')
print(res[0])
proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]])
print(ar, ma)
proc.nobs = nobs
# TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__
print(proc.ar, proc.ma)
print(proc.ar_roots(), proc.ma_roots())
from statsmodels.tsa.arma_mle import Arma
modn = Arma(x)
resn = modn.fit_mle(order=order)
moda = ARMA(x, order=order)
resa = moda.fit( trend='nc')
print('\nparameter estimates')
print('ls ', res[0])
print('norm', resn.params)
print('t ', res2.params)
print('A ', resa.params)
print('\nstandard deviation of parameter estimates')
#print 'ls ', res[0] #TODO: not available yet
print('norm', resn.bse)
print('t ', res2.bse)
from time import time
from statsmodels.tsa.arma_mle import Arma
from statsmodels.tsa.api import ARMA
import numpy as np
print("Battle of the dueling ARMAs")
y_arma22 = np.loadtxt(
r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt'
)
arma1 = Arma(y_arma22)
arma2 = ARMA(y_arma22)
print("The actual results from gretl exact mle are")
params_mle = np.array([.826990, -.333986, .0362419, -.792825])
sigma_mle = 1.094011
llf_mle = -1510.233
print("params: ", params_mle)
print("sigma: ", sigma_mle)
print("llf: ", llf_mle)
print("The actual results from gretl css are")
params_css = np.array([.824810, -.337077, .0407222, -.789792])
sigma_css = 1.095688
llf_css = -1507.301
results = []
results += ["gretl exact mle", params_mle, sigma_mle, llf_mle]
results += ["gretl css", params_css, sigma_css, llf_css]
t0 = time()
import statsmodels.api as sm
from statsmodels.sandbox import tsa
from statsmodels.tsa.arma_mle import Arma # local import
from statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print "\nExample 1"
print '----------'
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar,ma,1000,0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1,1), method='fmin')
print arma1res.params
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print res2.params
print res2.model.hessian(res2.params)
print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)
arest = tsa.arima.ARIMA(y1)
resls = arest.fit((1,0,1))
