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 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)
print resls[0]
print resls[1]
print '\nparameter estimate - comparing methods'
print '---------------------------------------'
print 'parameter of DGP ar(1), ma(1), sigma_error'
print[-0.8, 0.5, 0.1]
print 'mle with fmin'
print arma1res.params
print 'mle with bfgs'
print res2.params
print 'cond. least squares uses optim.leastsq ?'
errls = arest.error_estimate
print resls[0], np.sqrt(np.dot(errls, errls) / errls.shape[0])
err = arma1.geterrors(res2.params)
print 'cond least squares parameter cov'
#print np.dot(err,err)/err.shape[0] * resls[1]
#errls = arest.error_estimate
print np.dot(errls, errls) / errls.shape[0] * resls[1]
# print 'fmin hessian'
# print arma1res.model.optimresults['Hopt'][:2,:2]
print 'bfgs hessian'
print res2.model.optimresults['Hopt'][:2, :2]
print 'numdifftools inverse hessian'
print -np.linalg.inv(
ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2, :2]
print '\nFitting Arma(1,1) to squared data'
arma3 = Arma(y1**2)
res3 = arma3.fit(method='bfgs')
print '\nparameter estimate - comparing methods'
print '---------------------------------------'
print 'parameter of DGP ar(1), ma(1), sigma_error'
print [-0.8, 0.5, 0.1]
print 'mle with fmin'
print arma1res.params
print 'mle with bfgs'
print res2.params
print 'cond. least squares uses optim.leastsq ?'
errls = arest.error_estimate
print resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0])
err = arma1.geterrors(res2.params)
print 'cond least squares parameter cov'
#print np.dot(err,err)/err.shape[0] * resls[1]
#errls = arest.error_estimate
print np.dot(errls,errls)/errls.shape[0] * resls[1]
# print 'fmin hessian'
# print arma1res.model.optimresults['Hopt'][:2,:2]
print 'bfgs hessian'
print res2.model.optimresults['Hopt'][:2,:2]
print 'numdifftools inverse hessian'
print -np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2]
print '\nFitting Arma(1,1) to squared data'
arma3 = Arma(y1**2)
res3 = arma3.fit(method='bfgs')
print res3.params
###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
start_params = res_ols.params
start_params_mle = np.r_[-res_ols.params[:2],
res_ols.params[2:],
#res_ols.scale]
#areste.var()]
np.sqrt(res_ols.scale)]
#need to iterate, ar1 too large ma terms too small
#fix large parameters, if hannan_rissannen are too large
