def simulate_serial(nsim, nobs, ntoss, simpars):
# initialize arrays to store simulation results
MomentsAll = np.zeros((6, 7, nsim))
EEmatAll = np.zeros((3, nsim))
# perform simulations
for s in range(0, nsim):
# draw randowm errors
eps = np.random.randn(nobs) * sigma
# initialize variables
z = np.zeros((nobs + 1))
k = np.zeros((nobs + 1))
y = np.zeros(nobs)
r = np.zeros(nobs)
w = np.zeros(nobs)
i = np.zeros(nobs)
c = np.zeros(nobs)
# set starting values
k[0] = kbar
z[0] = eps[0]
# iteratively generate data
for t in range(0, nobs):
z[t + 1] = example_lfunc(z[t], eps[t], lpars)
k[t + 1] = example_tfunc(k[t], z[t], tpars)
y[t], c[t], i[t], r[t], w[t] = \
example_def(k[t+1], k[t], z[t], param)
# discard las observation for k & z
k = k[0:nobs]
z = z[0:nobs]
# stack data into a single array for moments calculation
data = np.stack((y, c, i, r, w, k, z), axis=1)
# discard first ntoss observations
data = data[ntoss:nobs, :]
# find moments
(Moments, MomNames) = calcmom(data,
means=True,
stds=True,
relstds=True,
corrs=True,
autos=True,
cvars=True)
# find Euler Errors
Xdata = k.reshape(len(k), 1)
Zdata = z.reshape(len(z), 1)
EErrs = EEcalc(Xdata, Zdata, efunc, epars, tfunc, tpars, lfunc, lpars)
MaxAbsEE = np.max(np.abs(EErrs))
MeanAbsEE = np.mean(np.abs(EErrs))
RootMeanSqEE = (np.mean(EErrs**2))**.5
EEmat = np.stack((MaxAbsEE, MeanAbsEE, RootMeanSqEE))
# Add results of current simulation to results arrays
MomentsAll[:, :, s] = Moments
EEmatAll[:, s] = EEmat
return MomentsAll, EEmatAll, MomNames
y = np.zeros(nobs)
r = np.zeros(nobs)
w = np.zeros(nobs)
i = np.zeros(nobs)
c = np.zeros(nobs)
k[0] = kbar * kstart
z[0] = eps[0]
for t in range(0, nobs):
z[t + 1] = example_lfunc(z[t], eps[t], lpars)
k[t + 1] = example_tfunc(k[t], z[t], tpars)
y[t], c[t], i[t], r[t], w[t] = example_def(k[t + 1], k[t], z[t], param)
k = k[0:nobs]
z = z[0:nobs]
# find Euler Errors
Xdata = k.reshape(len(k), 1)
Zdata = z.reshape(len(z), 1)
efunc = example_efunc
epars = param
tfunc = example_tfunc
lfunc = example_lfunc
EErrs = EEcalc(Xdata, Zdata, efunc, epars, tfunc, tpars, lfunc, lpars)
MaxAbsEE = np.max(np.abs(EErrs))
MeanAbsEE = np.mean(np.abs(EErrs))
RootMeanSqEE = (np.mean(EErrs**2))**.5
print 'Euler Error Summary Statistics'
print 'Maximum Absolute Euler Error:', MaxAbsEE
print 'Mean Absolute Euler Error:', MeanAbsEE
print 'Root Mean Squared Euler Error:', RootMeanSqEE