def __init__(self):
from scikits.statsmodels.datasets.sunspots import load
self.data = load()
self.rho, self.sigma = yule_walker(self.data.endog, order=4,
method="mle")
self.R_params = [1.2831003105694765, -0.45240924374091945,
-0.20770298557575195, 0.047943648089542337]
if __name__ == '__main__':
nobs = 50
ar = [1.0, -0.8, 0.1]
ma = [1.0, 0.1, 0.2]
#ma = [1]
np.random.seed(9875789)
y = arma_generate_sample(ar, ma, nobs, 2)
y -= y.mean() #I haven't checked treatment of mean yet, so remove
mod = MLEGLS(y)
mod.nar, mod.nma = 2, 2 #needs to be added, no init method
mod.nobs = len(y)
res = mod.fit(start_params=[0.1, -0.8, 0.2, 0.1, 1.])
print 'DGP', ar, ma
print res.params
from scikits.statsmodels.regression import yule_walker
print yule_walker(y, 2)
#resi = mod.fit_invertible(start_params=[0.1,0,0.2,0, 0.5])
#print resi.params
arpoly, mapoly = getpoly(mod, res.params[:-1])
data = sm.datasets.sunspots.load()
#ys = data.endog[-100:]
## ys = data.endog[12:]-data.endog[:-12]
## ys -= ys.mean()
## mods = MLEGLS(ys)
## mods.nar, mods.nma = 13, 1 #needs to be added, no init method
## mods.nobs = len(ys)
## ress = mods.fit(start_params=np.r_[0.4, np.zeros(12), [0.2, 5.]],maxiter=200)
## print ress.params
## #from scikits.statsmodels.sandbox.tsa import arima as tsaa
if __name__ == '__main__':
nobs = 50
ar = [1.0, -0.8, 0.1]
ma = [1.0, 0.1, 0.2]
#ma = [1]
np.random.seed(9875789)
y = arma_generate_sample(ar,ma,nobs,2)
y -= y.mean() #I haven't checked treatment of mean yet, so remove
mod = MLEGLS(y)
mod.nar, mod.nma = 2, 2 #needs to be added, no init method
mod.nobs = len(y)
res = mod.fit(start_params=[0.1, -0.8, 0.2, 0.1, 1.])
print 'DGP', ar, ma
print res.params
from scikits.statsmodels.regression import yule_walker
print yule_walker(y, 2)
#resi = mod.fit_invertible(start_params=[0.1,0,0.2,0, 0.5])
#print resi.params
arpoly, mapoly = getpoly(mod, res.params[:-1])
data = sm.datasets.sunspots.load()
#ys = data.endog[-100:]
## ys = data.endog[12:]-data.endog[:-12]
## ys -= ys.mean()
## mods = MLEGLS(ys)
## mods.nar, mods.nma = 13, 1 #needs to be added, no init method
## mods.nobs = len(ys)
## ress = mods.fit(start_params=np.r_[0.4, np.zeros(12), [0.2, 5.]],maxiter=200)
## print ress.params
## #from scikits.statsmodels.sandbox.tsa import arima as tsaa
examples_all = range(10) + ["test_copy"]
examples = examples_all # [5]
if 0 in examples:
print "\n Example 0"
X = np.arange(1, 8)
X = sm.add_constant(X)
Y = np.array((1, 3, 4, 5, 8, 10, 9))
rho = 2
model = GLSAR(Y, X, 2)
for i in range(6):
results = model.fit()
print "AR coefficients:", model.rho
rho, sigma = yule_walker(results.resid, order=model.order)
model = GLSAR(Y, X, rho)
par0 = results.params
print par0
model0if = GLSAR(Y, X, 2)
res = model0if.iterative_fit(6)
print "iterativefit beta", res.params
results.t() # is this correct? it does equal params/bse
# but isn't the same as the AR example (which was wrong in the first place..)
print results.t_test([0, 1]) # are sd and t correct? vs
print results.f_test(np.eye(2))
rhotrue = [0.5, 0.2]
rhotrue = np.asarray(rhotrue)